Run 2 Tracker Alignment Performance

EUROPEAN ORGANIZATION FOR NUCLEAR RESEARCH (CERN)

CERN-EP-2021-2032021/11/22

CMS-TRK-20-001

Strategies and performance of the CMS silicon trackeralignment during LHC Run 2

The CMS Collaboration*

Abstract

The strategies for and the performance of the CMS silicon tracking system alignmentduring the 2015–2018 data-taking period of the LHC are described. The alignmentprocedures during and after data taking are explained. Alignment scenarios are alsoderived for use in the simulation of the detector response. Systematic effects, relatedto intrinsic symmetries of the alignment task or to external constraints, are discussedand illustrated for different scenarios.

Submitted to Nuclear Instruments and Methods A

© 2021 CERN for the benefit of the CMS Collaboration. CC-BY-4.0 license

*See Appendix A for the list of collaboration members

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http://creativecommons.org/licenses/by/4.0

Contents 1

Contents1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12 The CMS detector . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 23 General concepts of alignment . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 34 Data sets . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5

4.1 Proton-proton collisions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 54.2 Cosmic ray muons . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8

5 Systematic misalignments . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 105.1 Validation of systematic distortions . . . . . . . . . . . . . . . . . . . . . . 105.2 Modelling and validation of global systematic distortions . . . . . . . . . 13

6 Alignment algorithms . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 176.1 MILLEPEDE-II . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 176.2 HIPPY . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 18

7 Alignment during data taking . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 207.1 Start-up alignments . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 207.2 Automated alignment . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 28

8 Alignment for physics analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 308.1 General strategy . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 318.2 Performance . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 358.3 Special runs . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 48

9 Alignment in simulation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 509.1 Derivation of the alignment . . . . . . . . . . . . . . . . . . . . . . . . . . 509.2 Validation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 519.3 Comparison of data and MC performance . . . . . . . . . . . . . . . . . . 55

10 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 60A The CMS Collaboration . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 65

1 IntroductionThe innermost subdetector of the CMS experiment constitutes the largest silicon tracker in theworld, both in terms of the total surface area and the number of sensors. To benefit from theexcellent resolution of the silicon sensors for measuring the trajectories of charged particles,the position and orientation of each sensor must be precisely measured. During the instal-lation procedure, a mechanical alignment yields a precision in the position of the tracker ofO(0.1 mm), which is much larger than the design hit resolution of O(0.01 mm). Therefore, afurther correction to the position, orientation, and surface deformations of the sensors needs tobe derived. This correction is commonly referred to as the spatial alignment of the tracker orsimply the tracker alignment. We will refer to the parameters of this correction as the trackeralignment constants. To maintain the targeted precision, the alignment constants must be up-dated regularly to include effects such as the ramping of the magnetic field or temperaturevariations. The approach employed by CMS consists in determining the alignment constantsby performing track fits with the corresponding track parameters unconstrained.

Previous publications [1, 2], covering the 2010–2012 LHC data-taking period (Run 1), showedhow the roughly 200 000 parameters necessary to describe the alignment of the tracker mod-ules were determined using track-based methods. Based on the same techniques, this article

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describes the strategies and recent developments utilized for the tracker alignment during theLHC data-taking period between 2015 and 2018, which we refer to as Run 2. We also quan-tify the performance of the alignment that is achieved at various stages based on observablessensitive to tracking.

After a short description of the CMS detector in Section 2, the concept of track-based align-ment is illustrated in Section 3, followed by a description of the data sets used to perform andvalidate the alignment in Section 4. We present studies that identify and minimize systematicdistortions, which can be left uncorrected by the track-based alignment, in Section 5. Two al-gorithms, MILLEPEDE-II and HIPPY, are used for the tracker alignment. The recent softwaredevelopments of these algorithms are summarized in Section 6, and the derivation of the align-ment constants during data taking is described in Section 7, taking as representative examplesthe start-up of Run 2 and the first alignments of the pixel detector in 2017 after the Phase-1upgrade [3, 4]. Section 8 focuses on the strategies developed to provide the best possible align-ment calibration for reprocessing the data before using them in physics analyses. Because ofthe higher intensity of the LHC compared to Run 1, a dedicated strategy was successfully de-veloped to include the fast changes in the local reconstruction conditions. In Section 9, thederivation of an alignment scenario for simulation is discussed, along with a comparison ofthe tracking performance between data and simulation. Section 10 summarizes the strategies,observations, and results.

2 The CMS detectorThe central feature of the CMS apparatus is a superconducting solenoid of 6 m internal diam-eter, providing a magnetic field of 3.8 T. Within the solenoid volume are a silicon pixel andstrip tracker, a lead tungstate crystal electromagnetic calorimeter, and a brass and scintillatorhadron calorimeter, each composed of a barrel and two endcap sections. Forward calorime-ters extend the pseudorapidity coverage provided by the barrel and endcap detectors. Muonsare measured in gas-ionization detectors embedded in the steel flux-return yoke outside thesolenoid.

Events of interest are selected using a two-tiered trigger system. The first level (L1), composedof custom hardware processors, uses information from the calorimeters and muon detectors toselect events at a rate of around 100 kHz within a fixed latency of about 4 µs [5]. The secondlevel, known as the high-level trigger (HLT), consists of a farm of processors running a versionof the full event reconstruction software optimized for fast processing, and reduces the eventrate to around 1 kHz before data storage [6].

During the 2016 (2017 and 2018) LHC running periods, the silicon tracker consisted of 1440(1856) silicon pixel and 15 148 silicon strip detector modules. After the 2016 data-taking period,the pixel detector was upgraded to its Phase-1 configuration. The upgraded pixel detectorfeatures one more layer in the barrel, and one more disk in each of the forward pixel endcaps,than the pixel detector that was in use up to the end of 2016 (Phase-0 pixel detector). Thisextended the acceptance of the tracker from a pseudorapidity range |η| < 2.5 to |η| < 3.0, andimproved the impact parameter resolution. Before the Phase-1 upgrade, the track resolutionswere typically 1.5% in transverse momentum (pT) and 25–90 (45–150) µm in the transverse(longitudinal) impact parameter for nonisolated particles of 1 < pT < 10 GeV and |η| < 1.4 [7].For nonisolated particles of 1 < pT < 10 GeV and |η| < 3.0, the track resolutions are typically1.5% in pT and 20–75 µm in the transverse impact parameter for data recorded after the Phase-1upgrade [8].

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The mechanical structure of the silicon tracker consists of several high-level structures: two halfbarrels in the barrel pixel tracker (BPIX), four half cylinders in the two forward pixel tracker re-gions (FPIX), two half barrels in the strip tracker inner barrel (TIB) and in the strip tracker outerbarrel (TOB), two endcaps in the tracker inner disks (TID) and in the tracker endcaps (TEC).For the Phase-0 (Phase-1) detector, the half barrels in the BPIX consist of three (four) layers andthe half cylinders in the FPIX consist of two (three) disks. In the barrel, groups of eight pixelmodules are mounted on rods arranged in cylindrical layers. The rods are mounted such thatthe modules of two adjacent ladders are rotated by 180 degrees around z with respect to eachother, thus having the silicon surface pointing inwards or outwards. In the FPIX, modules aresupported by blades arranged in a turbine-like geometry, each hosting two modules mountedback-to-back, pointing in opposite directions.

A more detailed description of the CMS detector, together with a definition of the coordinatesystem used and the relevant kinematic variables, can be found in Ref. [9].

3 General concepts of alignmentIn the track-based alignment approach, the alignment parameters p are derived by minimizingthe following χ2 function:

χ2(p, q) =tracks

∑j

hits

∑i

(mij − fij(p, qj)

σmij

)2

, (1)

where

• p represents the alignment parameters (also called alignables),

• q represents the track parameters (e.g. parameters related to the track curvature andthe deflection by multiple scattering [7]),

• m represents the measurements (e.g. hits) and f for the predictions, and

• σm represents the uncertainty in the measurements (e.g. local hit resolution, align-ment uncertainty).

The alignment parameters typically include eight or nine parameters per module, describingcorrections to the position, orientation, and surface deformations.

This χ2 can potentially have millions of parameters. To still be able to minimize it in that case,given an approximate set of alignment parameters p0 and the corresponding track parametersq0, we can first linearize the prediction term f in the χ2:

χ2(p0 + ∆p, q0 + ∆q) =tracks

∑j

hits

∑i

mij − fij(p0, q0 j)− ∆p∂ fij∂p (p0, q0 j)− ∆qj

∂ fij∂qj

(p0, q0 j)

σmij

2

.

(2)After some manipulation, this χ2 minimization can be reformulated into a system of tens orhundreds of thousands of linear equations, and treated like a matrix inversion problem:

C×(

∆p∆q

)= b, (3)

where C is a correlation matrix whose components are functions of ∂ fij/∂p, ∂ fij/∂qj, and σmij ,

and b is a source term whose components are functions of ∂ fij/∂p, ∂ fij/∂qj, σmij , and of the

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mi. The size of C corresponds to the total number of track and alignment parameters. Thematrix C is to be inverted; however, since this matrix is sparse and we are only interested inthe alignment parameters, it is not necessary to perform a full inversion. By means of blockmatrix algebra, Eq. 3 can be reduced to a linear equation system including only the alignmentparameters while keeping all the correlations from the tracks [10]:

C′ × ∆p = b′. (4)

where C′ (b′) is obtained from C (b) with a much smaller size, as all the steps of the ma-trix inversion related to the determination of the track parameters are not necessary for thedetermination of the alignment parameters. If the size of the matrix to invert is reduced toO(10 000) parameters, as is typical for the alignment of the pixel tracker, it can be invertedexactly. If the size of the matrix is larger, as is typical for the alignment of the whole pixeland strip tracker, alternative numerical approaches can be used to perform an approximatematrix inversion. The two implementations of the track-based alignment used at CMS will bediscussed further in Section 6.

Track-based alignment may suffer from different types of systematic biases inherited from thetracking algorithm, i.e. in f in Eq. (1), such as changes of conditions not included in the model.

During operation of the detector, changes in running conditions, such as changes of the mag-netic field or changes in temperature, are sometimes unavoidable. These changes happen afew times a year and may affect the alignment procedure. In general, it is essential to definethe interval of validity (IOV) of a set of alignment constants. For instance, after ramping downand then ramping up the magnet (magnet cycle), movements of the high-level structures ofO(1 mm) have been observed. In data-taking mode, the tracker is cooled down to tempera-tures close to −15 ◦C (−20 ◦C) for the 2015–2017 (2018) data-taking period. For maintenancepurposes, typically during a year-end technical stop (YETS), cooling may be interrupted. Thiscan potentially cause movements of the modules of O(10 µm).

Furthermore, the modules operate in a high-radiation environment, which affects their perfor-mance over time. One quantity that is sensitive to the irradiation dose and plays a role in thealignment calibration is the Lorentz drift, as illustrated in Fig. 1. It corresponds to the lateraldrift of the charge carriers in the silicon induced by the external magnetic field, and is orthog-onal to the electric field direction. Aside from the magnetic field, the magnitude of the driftdepends on the electric field, the mobility of the charge carriers, and the thickness of the activezone. Since these quantities are not constant, the measured hit position changes over time by∆x′ ∝ tan θLA, where θLA is the Lorentz angle. The sign of the shift depends on the orientationof the electric field, so that the shift in the hit position in modules pointing inward is oppositewith respect to this shift in outward-pointing modules. As a consequence of the higher irradia-tion dose close to the interaction point, the mobility of the charge carriers changes faster in thepixel detector than in the strip detector. These changes are corrected using a dedicated calibra-tion method and residual effects are corrected in the alignment procedure. The impact of theirradiation on the track reconstruction, and therefore on the alignment procedure, is illustratedin Fig. 2 and will be discussed in Section 8.

Another class of systematic biases arises from the internal symmetries of the alignment prob-lem, such as the cylindrical symmetry of the detector, or the fact that most tracks originate froma single region of space. This results in nonphysical geometrical transformations, also knownas weak modes (WMs). Systematic distortions will be further discussed in Section 5.

5

x'

z'

Δx'

θLAθtrk

E fie

ld

B field(3.8 T)

Charged track

Chargecluster

true hit

Figure 1: Sketch showing the transverse view of a silicon module working in a magnetic field B,with the backplane of the module located at the bottom. Here, x′ and z′ are the local coordinatesof the module. The grey lines in the shaded rectangle indicate the direction of the Lorentz drift,forming an angle θLA with the z′ axis. The blue line represents a charged particle traversing themodule with incident angle θtrk, and the magenta shaded area represents the volume in whichcharge carriers released by the ionization drift towards the electrodes at the top of the module.The blue-cyan (orange-red) dot represents the reconstructed hit if the Lorentz drift is (is not)included in the reconstruction. An example of a reconstructed charge cluster is shown by thevertical magenta bars above the module.

4 Data setsDifferent types of data sets are used in the alignment procedure and in its validation. In thissection, we first describe data sets from collision events, then data sets from cosmic ray muons.We generated corresponding simulated data samples; these are not expected to exactly de-scribe the observed data. However, it is important that the simulated samples cover the samephase space as the observed data, with similar event topologies and numbers of tracks, for thederivation of alignment scenarios in the simulation.

4.1 Proton-proton collisions

To achieve the desired statistical precision of track-based alignment, a large track sample of atleast several million tracks accumulated in the proton-proton (pp) physics run is indispensable.These events have tracks propagating outwards from the interaction point, which thereforecorrelate detector elements radially.

4.1.1 Inclusive L1 trigger

Events recorded with loose triggering conditions are referred to as belonging to the inclusiveL1 trigger data set. It consists of a sample of randomly chosen events passing an L1 trigger [5].Because of their large production rate, these events are particularly important during low-luminosity runs, as well as in the early stages of data taking for providing a sufficient amountof tracks for the alignment procedure. The track selection requires tracks to be reconstructedfrom a set of at least ten hits in the tracker. The tracks must have a momentum p > 8 GeV

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correct LAwrong LA

B field(3.8 T)

|residual| > 0

r

Figure 2: Sketch showing the transverse view of the Phase-0 barrel pixel subdetector, made ofsuccessive layers of silicon modules. The alternating orientation of the modules within eachlayer is indicated by the triangles. The blue (grey) circles represent the reconstructed hit po-sitions using incorrect (correct) Lorentz angles in the presence of a magnetic field B. The greycurve corresponds to a track built from the hits that were reconstructed with the correct Lorentzangles. Hits reconstructed with incorrect Lorentz angles are displaced in a direction defined bythe orientation of the module, increasing the residual distance between the hits and the track.

and pT > 1 GeV. The vast majority of the final-state particles have low pT, and their tracks areconcentrated in the high-η region, as shown in Fig. 3 (top row). This figure shows the pT andη distributions for tracks from the inclusive L1 trigger data set collected by the CMS detectorin 2018, after applying the track selection described above. The data are compared with MonteCarlo (MC) simulation for both low-pT and high-pT interactions, where pT is the scale in the2 → 2 matrix element calculation of the hard process. The events are simulated using PYTHIA

8.240 [11, 12] with the CP5 tune [13]. The low- and high-pT samples, which correspond toevents with pT in the range of 15–30 GeV and 1000–1400 GeV, respectively, are used as twoopposite reference points and the data naturally fall in between. The small fraction of eventscoming from the high-pT interactions produces the harder pT spectrum and the more central ηdistribution observed in data with respect to the simulation.

4.1.2 Isolated muons

Another suitable data set for the alignment procedure consists of isolated high-pT muons fromleptonic decays of W bosons, since they are recorded with very high efficiency and their trackparameters can be measured very precisely in the detector. This data set consists of eventspassing the selection of at least one among several single-muon triggers. These triggers requirethe presence of an isolated muon and differ in the pT threshold applied. Tracks of muon can-didates reconstructed both in the silicon tracker and in the muon spectrometer, termed globalmuons, are selected if they have at least ten hits in the tracker, including at least one in thepixel detector. Events must have exactly one isolated muon candidate with pT > 5 GeV. Anisolation condition is imposed on the muon by requiring it be separated from the axis of anyjet candidate by ∆R > 0.1, where ∆R =

√(∆η)2 + (∆φ)2 and ∆η and ∆φ are differences in the

pseudorapidity and the azimuthal angle, respectively. Isolated muons cover a different phasespace with respect to collision tracks from the inclusive L1 trigger data set, because they are

4.1 Proton-proton collisions 7

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Figure 3: Normalized track pT (left) and η (right) distributions for the inclusive L1 trigger (top),Z → µµ (middle), and interfill cosmic ray muon (bottom) data sets in arbitrary units. Datacollected with the CMS detector in 2018 and used for the final alignment in that year (solidblack circles) are compared with the simulation (solid coloured lines). Distributions in data areobtained from a sample of 11× 106, 55× 106, and 3.4× 106 tracks for the inclusive L1 trigger,Z → µµ, and cosmic ray muon data sets, respectively. For the inclusive L1 trigger data set,data are compared with two sets of simulated QCD events with different ranges of transversemomentum transfers pT. The green line corresponds to pT between 15 and 30 GeV, whereas themagenta line corresponds to pT between 1000 and 1400 GeV. The inset in the pT distribution ofthe inclusive L1 trigger data set (top left) shows the same distribution with a logarithmic scalefor the y axis. No correction for the limited modelling of the trigger efficiency in the simulationhas been applied for the Z → µµ data set. The statistical uncertainty is smaller than the symbolsize and therefore imperceptible.

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characterized by a harder pT spectrum and hence a more central η distribution.

4.1.3 Dimuon resonances

This data set is formed by events passing the selection of a collection of double-muon trig-gers with different muon pT and isolation requirements. Tracks from the decay products ofwell-known dimuon resonances are particularly valuable for alignment purposes, because ad-ditional information from vertex and invariant mass constraints can be added to Eq. (1) to con-strain certain kinds of systematic distortions, especially those that bias the track momentum.By considering different resonances we can connect different groups of modules, because thedifference in ∆φ between the two tracks depends on the boost of the mother particle. For thisreason, muon tracks from both Υ meson and Z boson decays are included. To target Υ → µµevents, the applied selection requires track pT > 3 GeV and a dimuon invariant mass in therange 9.2 < mµµ < 9.7 GeV. To select muon pairs from Z boson decays these requirementsare pT > 15 GeV and 85.8 < mµµ < 95.8 GeV. The pT and η distributions for tracks recordedin 2018 that satisfy the Z → µµ event selection are shown in Fig. 3. The MC events for com-parison with data are generated using MADGRAPH5 aMC@NLO 2.6.0 [14], which is interfacedwith PYTHIA 8.240 to simulate parton showering and hadronization. The lower event yields indata in the central η region are due to a known trigger inefficiency, which is not included in thesimulation.

4.2 Cosmic ray muons

Cosmic ray muons, referred to as cosmics or cosmic events, recorded by the CMS detector areused for detector commissioning and calibration. Before turning on the magnetic field, eventsare recorded during the Cosmic RUns at ZEro Tesla (CRUZET). Cosmic ray muon tracks arealso recorded in the 3.8 T magnetic field provided by the CMS solenoid, during the CosmicRuns At Four Tesla (CRAFT). Tracks from cosmic ray muons are crucial for the derivation ofthe alignment constants for two main reasons. First, they can be recorded before the start ofLHC collisions, and are therefore employed to derive the first alignment corrections after ashutdown period, as described in Section 7.1. Second, they have a very different topologycompared with collision tracks. Unlike collision tracks, tracks from cosmic ray muons cross thewhole detector and connect modules located in the top and bottom halves of the tracker. Thisbreaks the cylindrical symmetry typical of collision tracks and helps to constrain several classesof systematic distortions. Figure 3 (bottom row) shows the pT and η distributions for cosmicray muons in which the asymmetry in η is attributed to the location of the CMS cavern shaft.The MC events in the figure, used for comparison with data, are simulated using the cosmicmuon generator CMSCGEN [15].

Throughout Run 2, cosmic ray muon events were recorded before the start of LHC collisions indedicated commissioning runs and in the time intervals between two LHC fills (interfill runs).Since 2018, it has been possible to record cosmic events during collision data taking. Figuresin this paper that contain data from cosmic events are indicated with the label “cosmic rays”instead of “pp collisions”.

4.2.1 Commissioning and interfill runs

A total of 1.72× 107 and 1.33× 107 cosmic ray muon events were collected with the CMS de-tector during commissioning and interfill runs, respectively, from 2016 to 2018. These eventsare selected using an unprescaled single-muon trigger with no pT threshold applied. Figure 4shows the average rate of cosmic ray muon events recorded by the CMS detector in this time pe-riod. Cosmic events are recorded using dedicated muon triggers. The tracks are reconstructed

4.2 Cosmic ray muons 9

using three different algorithms, which are described in Ref. [16]: combinatorial track finder,cosmic track finder, and road search. Reconstructed tracks obtained by the combinatorial trackfinder algorithm are used for the alignment procedure, and are required to have at least sevenhits, of which at least two must be in either the pixel detector or in stereo module pairs. Stereomodule pairs consist of two strip modules mounted back-to-back, with their strips aligned ata relative angle to provide a measurement of both the r-φ and the r-z coordinates. The averagetrack rates after this selection are also shown in Fig. 4, both inclusively and separately for trackswith at least one valid hit in a given tracker partition. A systematic study of the average trackrates is essential for estimating the duration of cosmic data collection during commissioningand of the interfill runs needed to accumulate a sufficient number of tracks for the alignmentprocedure. Additional quality requirements are applied to the tracks used in the alignment fit;events with more than one track are rejected, as are tracks with p < 4 GeV.

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Figure 4: Average event rates of cosmic ray muon data recorded with the CMS tracker duringthe years 2016, 2017, and 2018, obtained as explained in the text. The statistical uncertainty inthe measured rates is negligible and is not shown in the figure.

4.2.2 Cosmics during pp collisions

To increase the number of cosmic ray muon tracks used in the alignment procedure, an effortwas made to collect cosmics during collisions (CDC). Dedicated trigger sequences, which relyon the longer trajectory of muons from cosmic rays inside the detector with respect to muonsbeing produced in the pp interaction region, were developed for this. The typical time of flightof a cosmic ray muon passing through the whole detector is ≈30 ns, larger than the interval of25 ns between two consecutive bunch crossings. A cosmic ray muon candidate can be identi-fied by requiring two consecutive signals of the global muon trigger in a back-to-back topology.In the L1 trigger, only muon candidates with pT > 3 GeV in the central region, |η| < 1.2, of thedetector are retained to reduce the background from low-pT muon tracks from pp interac-tions. A larger fraction of low-pT background tracks is rejected after the muon reconstructionis performed by the HLT, and the kinematic requirements are tightened to keep the trigger ratebelow a threshold of O(5− 10 Hz). Owing to the dynamical dependence of the trigger rate onthe number of additional pp interactions from the same or nearby bunch crossing (pileup), twoCDC triggers were introduced in the HLT. A pT threshold of 10 (5) GeV is required in the main(low-pileup) trigger.

These dedicated CDC triggers were deployed in July 2018 and were active until the end of thepp collision run of that year. Approximately 700 000 tracks collected with the CDC triggerspassed the alignment selection criteria for cosmic ray muon tracks.

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5 Systematic misalignmentsSystematic shifts of the assumed positions of the silicon modules of the tracker, when com-pared with the actual positions of the active elements, can occur. Such shifts will be calledsystematic distortions, or misalignments, of the tracker geometry. These systematic misalign-ments may cause biases in the track reconstruction and this can have a negative impact onphysics measurements. Therefore, a dedicated programme of studies of such systematic dis-tortions was developed. The WMs mentioned in Section 3 form a particular class of systematicdistortions. These are transformations that change a set of valid tracks into another set of validtracks and satisfy ∆χ2 ≈ 0. Although such a transformation does not affect the individual trackparameters used in the alignment procedure, it may affect certain topologies of the tracks orcorrelations between tracks that are later used in physics measurements.

The most obvious example of a WM is a global movement of the whole detector, but more sub-tle effects are possible. The fact that all collision tracks come from the centre of the detector andthat the detector is symmetric around the beam axis may cause certain WM biases that leavethe χ2 of the individual collision tracks invariant. Such a systematic distortion is not necessarilya WM, but the effect may be especially large in the direction with the weakest constraints.

In this section, we first present the methods used to detect the presence of systematic distortionsin the alignment constants, then we review nine canonical systematic distortions.

5.1 Validation of systematic distortions

Several validations are used to check the effect of misalignments and determine whether a par-ticular set of alignment constants performs well. A validation is essentially a projection of thealignment performance onto a variable of interest. The quantities we choose to study typicallyhave a known value under perfectly aligned conditions. For example, a distribution of resid-uals is expected to peak at 0 with a given width. The difference in parameters between twohalves of a cosmic ray track is also expected to be 0 on average. The mass of a reconstructedZ boson should be around 91.2 GeV. By detecting deviations from these expected values, espe-cially deviations as functions of the track location or direction, we can search for biases.

5.1.1 Geometry comparison

Once the alignment fit has been performed, the new geometry is compared with a reference ge-ometry, such as the design geometry or a previously aligned geometry. Systematic differencesin such a comparison may reveal distortions in the tracker geometry. Although it is not possibleto assess the validity of systematic shifts in the module positions from geometry comparisonsalone, they may serve as a guide and visualization of possible effects in the tracker. Certaindistortions may be known to be unphysical from the detector design constraints, and wouldform an early warning of biases in the alignment procedure prior to more detailed tests withthe reconstructed track data. The geometry comparison tool was derived from the tools devel-oped for the optical survey constraint within the HIPPY algorithm discussed in Section 6. Thesetools match two geometries by translating and rotating certain structures before the differencesbetween the geometries are calculated. These differences are treated as survey residuals in thealignment algorithm [1]. The global shift and rotation of large structures are removed and themodule displacements ∆z, ∆r, and ∆φ are measured with respect to the reference geometry asa function of z, r, and φ. The other coordinates (x, y, and z) and three angular rotations can bevisualized in this manner as well.

Geometry comparison validation is performed without any reconstructed track data and can

5.1 Validation of systematic distortions 11

be applied with reference to any prior geometry, such as design, survey, or previously alignedtrack-based geometry.

5.1.2 Cosmic ray muon track validation

The track parameter resolutions can be validated by independently reconstructing the upperand lower portions of cosmic ray muon tracks that cross the tracker and comparing the trackparameters at the point of closest approach to the nominal beamline. We will refer to thisprocedure as the cosmic ray muon track split validation. This method is powerful becausewe know that the two halves of a given cosmic ray track should have the same parameters atthe point closest to the nominal beamline, while each half of a track mimics a regular collisiontrack originating from that point. Systematic differences between the track halves can indicatea misalignment.

Cosmic ray muon tracks and collision tracks have different topologies. Therefore, systematicdistortions that may appear as WMs with collision tracks may be well constrained or visiblewith cosmics. In particular, the fact that these tracks do not originate at the centre of the detectormeans they connect the top and bottom halves of the detector directly through a single track.Such a connection is not possible with tracks originating from the beam collision point. Thiseffect is shown in Fig. 5.

z

r true distorted

z

r true distorted

Figure 5: Diagram demonstrating distortions of the tracker geometry that may not affect theconsistency of a reconstructed collision track with the measured hits (left), but introduce akink when reconstructing the two halves of a cosmic ray muon track (right), leading to aninconsistency. This illustrates the telescope effect from Table 1.

Cosmic ray muon track validation can be performed without data from beam collisions andserves as an early validation of the detector geometry before LHC operation starts. It remainsa powerful tool during collision data taking because of the unique topology of the cosmic raymuon tracks.

5.1.3 Overlap of hits within the same layer of modules

The overlap validation monitors the alignment by using hits from tracks passing through re-gions where modules overlap within a layer of the tracker. It can be performed either with thecosmic data or with data from beam collisions. Tracks are required to have two hits in sepa-rate modules within the same layer. In this method we take advantage of the small distancebetween the two hits, and therefore the small uncertainty in the track parameter propagationbetween the two modules. The double difference in estimated and measured hit positions isvery sensitive to systematic deformations. Unexpected deviations between the reconstructedhits and the predicted positions can indicate a misalignment. This is characterized by a nonzeromean of the difference of residuals.

An illustration of the overlap measurements is shown in Fig. 6. The quantity of interest is

12

the difference of residuals calculated as ((hitA − predictionA) − (hitB − predictionB)), wherehitA,B refers to the position of a hit in module A or B, and predictionA,B refers to the positionof a predicted impact point of the track in module A or B derived from its fit using measure-ments in other modules. The advantage of the overlap method is that most uncertainties in thetrack propagation are cancelled in the difference (predictionA− predictionB). The difference ofresiduals is expected to be zero on average for a perfectly aligned detector. A positive shift inthe mean is expected for expansion and a negative shift for contraction.

In Fig. 6, the overlap between modules A and B constrains the circumference of the detector,and therefore its radial scale, when measured for all pairs of modules. Figure 7 shows anexample of the module overlaps in the φ and z directions for three representative modules inthe first layer of the BPIX. The overlap between modules A and B constrains the circumferenceof the detector, as in Fig. 6. The overlap between modules A and C constrains the distancebetween modules in the z direction, and therefore the longitudinal scale.

expected position (nominal)

actual position (expansion)

hitB

hitA

predictionB

predictionA

hitB=predictionB

hitA=predictionA

A

A

B

B

Figure 6: Diagram demonstrating the overlap regions of two representative modules A and B.In the upper diagram, the predicted track impact points (green circles) and the actual hits withcharge depositions (red and blue circles) do not coincide because of a wrong prediction of themodule positions. In the lower diagram, the actual module positions are shown for the geome-try with radial expansion, and the predicted impact points and hits coincide. Uncertainties dueto track propagation are ignored in this illustration, but are greatly reduced in the differenceof residuals as discussed in the text. The green dashed circles in the lower diagram indicatepredicted impact points from the nominal geometry in the upper diagram.

5.1.4 Dimuon validation

In an ideally aligned tracker, the reconstructed X → µµ invariant mass should be minimallydependent on where in the detector the muons travel. Therefore, the quality of the set of align-ment constants can be assessed by looking for biases in the reconstructed mass of a knownresonance X. Any resonance can be used, but in practice we primarily consider Z boson de-cays into muons. This is because Z bosons are often produced with a relatively small boost,which results in the two muons passing through opposite ends of the tracker. Figure 8 showsan example of a systematic distortion to which Z → µµ decays are very sensitive (twist distor-tion, described in Section 5.2).

Each selected event, with its reconstructed mass, is placed into a bin depending on the η andφ of the muons. The mass distribution of each bin is then fit with a Gaussian function, and themean of this function is recorded as the reconstructed mass in that bin. The bins are then usedto construct profiles of the invariant mass as a function of η or φ. Misalignment in the trackermay be detected if the mean reconstructed mass strays from the expected value of 91.2 GeV,either uniformly or as a function of η and φ.

5.2 Modelling and validation of global systematic distortions 13

30− 25− 20− 15− 10− 5− 0 5 10Z Position (cm)

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Figure 7: Diagram demonstrating the overlap regions of three representative modules A, B,and C in the first layer of the barrel pixel detector. The y-z view (left) and y-x view (right)are shown for the same modules. The overlap hits are indicated with the blue (inner) and red(outer) crosses and appear in tracks with hits in two consecutive modules in the same layer ofthe detector. The black cross represents the interaction point. The overlap between modules Aand B constrains the distance between modules in the φ direction, whereas the overlap betweenmodules A and C constrains the distance between modules in the z direction.

z

y true distortedtruedistorted

x

y mres ≠ mµµ

B field(3.8 T)

Figure 8: Diagrams demonstrating distortions of the tracker geometry in the r-z view (left) andin the x-y view (right) with the reconstructed muon pair from a Z → µµ decay. The invariantmass of the pair of muons deviates from the expected value and becomes a function of the trackparameters. This illustrates the twist effect from Table 1.

Dimuon validation is performed with the LHC collision data after a sufficiently large sampleof Z → µµ events has been accumulated. Therefore, this validation is powerful in stable oper-ating conditions.

5.2 Modelling and validation of global systematic distortions

To study systematic distortions, and WMs in particular, we introduce nine first-order deforma-tions natural for the cylindrical geometry of the CMS tracker and parameterize them with sim-

14

ple models described by a single parameter ε for each distortion. The systematic displacementsfrom the reference geometry in ∆z, ∆r, and ∆φ are functions of z, r, and φ, with an overall scal-ing given by ε. The functional forms used to generate each systematic misalignment are listedin Table 1.

Table 1: The nine basic systematic distortions in the cylindrical system, with the names ofeach systematic misalignment, the function by which the misalignment is generated, and avalidation type sensitive to the misalignment. The parameter z0 = 271.846 cm is half of thelength of the CMS tracker, and φ0 is an arbitrary constant phase.

∆z ∆r ∆φ

z expansion bowing twistvs. z ∆z = εz ∆r = εr(z2

0 − z2) ∆φ = εzoverlap overlap Z → µµ

telescope radial layer rotationvs. r ∆z = εr ∆r = εr ∆φ = εr

cosmics overlap cosmics

skew elliptical sagittavs. φ ∆z = ε cos(φ + φ0) ∆r = εr cos(2φ + 2φ0) ∆φ = ε cos(φ + φ0)

cosmics cosmics cosmics

The sign of ε is critical in the description of its value for misalignments. To save computingtime, MC simulations are always performed using the ideal geometry, and the track reconstruc-tion is performed with a possibly misaligned geometry. That is, the detector position remainsfixed to the ideal geometry, and the geometry used in the reconstruction changes. When dis-cussing data, the opposite convention is more natural: the geometry used in the reconstructionis initially fixed and the detector itself moves. Taking the radial misalignment as an example, avalue of ε > 0 means that the geometry used for reconstruction is expanded in the r directionwith respect to the geometry used during data taking. If this happens in data, we call it a radialcontraction, because the detector has moved with respect to the expected position.

The nine basic systematic distortions summarized in Table 1 are not necessarily WMs whenconsidering all possible topologies of tracks. We found that cosmic ray muon track, overlap,and dimuon validation are sufficient to detect these global, coherent movements of modules.This is illustrated in Fig. 9, where for each of the nine misalignments a representative validationusing one of the three techniques is shown. In all cases, the five distributions are constructedusing an MC simulation where the true positions of the modules are known. The distributionscorrespond to ε = 0 (no misalignment) and two nonzero values of ε, both positive and negative.Where appropriate, the mean (µ) and root-mean-square (RMS) of the distributions are alsogiven in the figure legends. The results of this section are crucial when deriving the alignmentparameters in pp collision data, as discussed in Section 8.

The uniform misalignment of the tracker in the z direction is known as z expansion (or con-traction). In the BPIX, z expansion can be detected using overlapping sensors in the same layer.This validation is not possible with the silicon strip modules because there is no precise mea-surement of the z coordinate. We find that a change in ε causes a shift in the mean of thedistributions for overlaps in the z direction. The design of the silicon pixel detector does notprovide a z overlap for modules at the same azimuthal angle, but it is possible for modulesthat are near in φ. Figure 9 (top row, left) shows the distribution of differences of residuals inthe overlapping modules in the z direction with modules overlapping in the z-direction in theBPIX for cosmic muon events in MC simulation. The z expansion misalignment is tested with

5.2 Modelling and validation of global systematic distortions 15

100− 50− 0 50 100

m]µ) [B

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z-Expansionmµ = 4.95 µ, -410× = -2.02∈mµ = 2.40 µ, -410× = -1.01∈

mµ = -0.46 µideal, mµ = -3.48 µ, -410× = 1.01∈mµ = -6.36 µ, -410× = 2.02∈

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= -0.906 rms = 1.16µ, -410× = -2.5∈ = 0.0034 rms = 1.11µideal,

= 0.911 rms = 1.20µ, -410× = 2∈ = 1.74 rms = 1.34µ, -410× = 5∈

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mµ = -0.89 µideal, mµ = -13.94 µ, -2 cm-910× = 3.39∈mµ = -26.60 µ, -2 cm-910× = 6.77∈

2.5− 2− 1.5− 1− 0.5− 0 0.5 1 1.5 2 2.5

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e/GeV-310× = 1.93µ, -1 cm-610× = -9.43∈ e/GeV-310× = 1.05µ, -1 cm-610× = -4.72∈

e/GeV-310× = 0.013µideal, e/GeV-310× = 1.06µ, -1 cm-610× = 4.72∈ e/GeV-310× = 1.94µ, -1 cm-610× = 9.43∈

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rad-410×, A = 3.0-410× = 2.5∈ rad-410×, A = 5.6-410× = 5∈

Figure 9: Validation of the nine basic systematic distortions summarized in Table 1 using re-constructed MC simulations with five variations of the misalignment parameter ε in each case.The ideal geometry in MC simulation corresponds to ε = 0. The horizontal lines show the un-certainty on the average of a measurement in a given bin. The most sensitive validation out ofcosmic ray muon track, overlap, or dimuon validation is employed in each case, as discussedin more detail in the text and as indicated in Table 1. In the bottom row, the formulae indicatethe functional form of the fit used to extract the parameter quoted in the legend, which can beused to quantify the distortion. The convention for the sign of ε is discussed in the text andcorresponds to a distortion in the geometry used for the reconstruction of MC events. This isopposite to the sign of the distortion if it were to be introduced in simulation of the detectorcomponents traversed by the charged particles.

16

ε = −2.02× 10−4, −1.01× 10−4, 0, 1.01× 10−4, and 2.02× 10−4. Constraining the z expansionin the strip detector is a more challenging task. For example, the global z position of the siliconmodules in the endcap detectors is weakly constrained. Certain biases in the track reconstruc-tion may appear if inactive material is not fully included in the detector model. This may leadto distortions in the detector geometry appearing in the form of a z expansion.

Radial expansion (or contraction) is the uniform misalignment of the tracker in the r directionas a function of r (r → r + εr). Because of the uniform and symmetric nature of this mis-alignment, it is not easily detected with cosmic ray muon track splitting or Z → µµ decays.However, it is easily detected using the overlap validation, since in the case of a radial expan-sion, modules that overlap in the radial direction will move apart uniformly. Therefore, thedifference between the true and the predicted hit locations in two overlapping modules is agood indicator of a radial expansion or contraction. The linear relationship between the meanof the overlap validation figures and the magnitude of the radial misalignment are used to cat-egorize the presence of radial expansion or contraction in pp collision data. Figure 9 (top row,middle) shows the distribution of overlaps in the φ direction for modules overlapping in the φdirection in the BPIX for collision events in MC simulation. The MC events are simulated withε = 5× 10−4, 2.5× 10−4, 0, −2.5× 10−4, and −5× 10−4.

Twist is the misalignment of the tracker in the φ direction as a function of z. As such, twistshows up clearly in the Z → µµ validation, and also in the overlap validation. The parameterused is the slope of the invariant mass mµµ vs. ∆ηµµ distribution. It ranges from ∆ηµµ = −2 to+2, as the distribution becomes nonlinear for larger values of ∆ηµµ . Figure 9 (top row, right)shows the profile of invariant mass mµµ vs. ∆ηµµ for Z → µµ events in MC simulation. The MCevents are simulated with ε = 2.04× 10−6, 1.02× 10−6, 0,−1.02× 10−6, and−2.04× 10−6 cm−1.

The telescope effect is the uniform misalignment of the tracker in the z direction as a functionof r (z → z + εr). This creates concentric rings that are offset in the z direction, and thismisalignment can be visualized by imagining an actual telescope. Because of its z dependence,the telescope effect is identified primarily using the reconstruction of cosmic ray muon tracks.Figure 9 (middle row, left) shows the distribution of ∆θ/σ(∆θ) for cosmics in MC simulation.The MC events are simulated with ε = 5× 10−4, 2.5× 10−4, 0, −2.5× 10−4, and −5× 10−4.

Bowing is the misalignment of the tracker in the r direction as a function of z. It is similar to theradial expansion, and differs only by the fact that the bowing effect is a function of z. Figure 9(middle row, middle) shows the distribution of overlaps in the φ direction with modules over-lapping in the φ direction in the TOB for cosmic ray muon tracks in MC simulation. The MCevents are simulated with the ideal detector geometry and reconstructed using five geometries,corresponding to the bowing misalignment with ε = 6.77× 10−9, 3.39× 10−9, 0, −3.39× 10−9,and −6.77× 10−9 cm−2.

Layer rotation is the misalignment of the tracker in the φ direction as a function of r. Theouter layers twist with a different magnitude to that of the inner layers. This distortion iseasily picked up with cosmic ray muon track splitting, since we can see a change in trackcurvature between the two track halves. As such, we take the mean of a value proportional tothe curvature for each value of ε. Figure 9 (middle row, right) shows the distribution of ∆(q/pT)for cosmic events in MC simulation. The MC events are simulated with ε = 9.43 × 10−6,4.72× 10−6, 0, −4.72× 10−6, and −9.43× 10−6 cm−1.

Skew is the misalignment of the tracker in the z direction as a function of φ. Because of the φdependency, it can be detected with cosmic ray muon track splitting. The distribution of ∆dzvs. φ can be fit with a hyperbolic tangent function, A × tanh(B(φ + C)), from which we can

17

extract ε. Figure 9 (bottom row, left) shows the profile of ∆dz/√

2 vs. φ for cosmic events in MCsimulation. The MC events are simulated with ε = 5.5× 10−2, 2.25× 10−2, 0, −2.25× 10−2,and −5.5× 10−2 cm.

Elliptical distortion is the uniform misalignment of the tracker in the r direction as a function ofφ (r → r + rε cos(2φ + δ)). Because of its φ dependency, elliptical distortion is easily detectedwith cosmic ray muon track splitting. This misalignment is especially clear in the modulationof the difference in the impact parameter ∆dxy as a function of the azimuthal angle of the track.We fit a sinusoidal function to this modulation, ∆dxy = −A × sin(2φ + B), and find a linearrelationship between A and ε. Figure 9 (bottom row, middle) shows the profile of ∆dxy/

√2

vs. φ for cosmic ray muon events in MC simulation. The MC events are simulated with ε =5× 10−4, 2.5× 10−4, 0, −2.5× 10−4, and −5× 10−4.

Sagitta distortion is the uniform misalignment of the tracker in the φ direction as a function ofφ. As with the elliptical misalignment, the φ dependence in the sagitta distortion means it canbe detected with cosmic ray muon track splitting validation. The effect of the misalignment canbe seen in distributions of ∆φ vs. φ. The distributions of ∆φ vs. φ are fit with a cosine function,∆φ = −A cos(φ + B), from which we can extract ε. Figure 9 (bottom row, right) shows thedistribution of ∆φ vs. φ for cosmic events in MC simulation. The MC events are simulatedwith ε = 5× 10−4, 2.5× 10−4, 0, −2.5× 10−4, and −5× 10−4.

As the above studies show, various systematic distortions in the tracker geometry can be de-tected using combinations of different types of tracks and hits. Therefore, it is essential tocombine all this information in the alignment procedure, which will be discussed in the nextsection. Balanced information in the input to the alignment procedure would ensure that suchdistortions are not present in the tracker geometry prepared for the reconstruction of tracks.

6 Alignment algorithmsThe CMS Collaboration uses two independent implementations of the track-based alignment,MILLEPEDE-II and HIPPY. The two algorithms follow different strategies. The earlier imple-mentations of both algorithms were described in Refs. [1, 2, 17]. In this section, we outline theimprovements in the two algorithms motivated by the needs of the alignment procedure of theCMS tracker during Run 2.

6.1 MILLEPEDE-II

The MILLEPEDE-II algorithm [10, 18, 19] has been discussed in the context of CMS in Ref. [2]. Itis still being developed further to meet the growing user needs; in addition to CMS, the Belle-IIexperiment [20] is a main user driving the developments. In this section, we review the mainalgorithms implemented in the software and describe recent improvements.

The MILLEPEDE-II algorithm allows determination of the position, the orientation, and the cur-vature of the tracker modules. The algorithm consists of two steps:

MILLE This program has to be integrated into the track fitting software of the specific ex-periment. For each track the independent residuals with errors and the derivatives ofthe track (local) and module (global) parameters from Eq. (2) have to be calculated andstored in custom binary files. The track fitting method has to fit all hits simultaneously,providing the complete covariance matrix of all track parameters. This can not be pro-vided by the standard Kalman filter [21], because it is a sequential method. Therefore the

18

mathematically equivalent general broken lines method [22, 23] has been implemented.This is a refit of the trajectory defined by the track parameters from the Kalman filter atone given hit, e.g. the first. The output to binary MILLE files contains the subset of thetrajectory attributes that are needed by MILLEPEDE-II. Only the global derivatives have tobe added.

PEDE This is an experiment-independent Fortran program that builds and solves the linearequation system from Eq. (4). It reads a text file with steering information and the trackswith the hit information from the MILLE binary files to perform the local (track) fits toconstruct the global matrix C′. This symmetric matrix is stored in full (lower triangularpart) or sparse (only nonzero parts) mode. Several solution methods are implemented.An overview is given in Table 2.

Table 2: List of the main solution methods implemented in MILLEPEDE-II. The computationtime is given as a function of the number of parameters n and the number of internal iterationsnit if applicable. The type of solution delivered by the algorithm is also shown.

Method Computing time Solution type Error calculationInversion (Gauss–Jordan) ∼n3 Exact YesCholesky decomposition ∼n3 Exact Skipped (for speed)MINRES [24, 25] ∼n2 × nit Approximate No

Compared with the version used previously by CMS [2], the most important technical improve-ments used for the alignment fits described in this paper are:

1. The migration from Fortran 77 to Fortran 90 allowing for dynamic memory management.

2. The implementation of the solution of problems with constraints by elimination, in addi-tion to Lagrange multipliers. Especially for large problems where an approximate solu-tion is obtained [24], elimination shows superior numerical performance.

3. The analysis of the input (global parameters and constraints) for optional factorization ofa large problem into smaller ones using block matrix algebra.

4. Alignable objects are, in general, described by several global parameters. These globalparameters appear together in the binary files, and are now split into groups by relying onthe adjacent global (user-defined) labels with which the parameters appear. This meansthe global matrix is organized as a collection of block matrices instead of a collectionof single values. The size corresponds to the two contributing parameter groups. Byarranging the matrix in this way, operations on the global matrix are sped up using thecaching and vectorization options available on modern processors. This helps especiallyin the case of sparse storage, since typically 10–30% of the elements of the global matrixare nonzero.

In Table 3, we illustrate the amount of running time used by PEDE to solve the linear equationsystem from Eq. (4); a record typically corresponds to a track or to a pair of tracks coming froma resonance decay.

6.2 HIPPY

The HIPPY algorithm is based on the hits-and-impact-points algorithm [26, 27] with additionalfeatures introduced using the constraints developed for the BaBar track-based alignment [28].

6.2 HIPPY 19

Table 3: Examples of PEDE wall time (time taken from start of the program to end) for somelarger alignment campaigns on a dedicated test machine (Intel Xeon E5-2667 @ 3.2 GHz, 256 GBmemory @ 51 GB/s).

Number of Number of Number of Matrix size [GB] Wall time [s]global parameters constraints records (sparse) (10 threads)

217500 138 4.46× 107 44 8.4× 103

213900 1782 2.90× 107 85 6.8× 103

576000 942 5.20× 107 218 4.4× 104

It has been used extensively during commissioning of the CMS tracker [17] and during theCMS start-up period in Run 1 [1, 2]. Further improvements were introduced during Run 2, asdescribed below. The improved algorithm is now named hits-and-impact-points-past-year-1(HIPPY).

The main distinguishing feature of the HIPPY algorithm, compared with MILLEPEDE-II, is itslocal nature. The position and orientation of each sensor are determined independently of theother sensors. This approach has advantages and disadvantages compared to MILLEPEDE-II.One disadvantage is that multiple iterations of running the algorithm are required to solvecorrelations between the sensor parameters. The number of iterations can be several dozenup to a hundred. This means multiple runs of the CPU-expensive track fits are needed, whichlimits the practical application of this algorithm. Advantages include the native integrationwith CMS software, immediately providing features such as the CMS Kalman filter code fortrack propagation without additional development. As a result, any constraint, such as massor vertex constraints, implemented in the CMS software can be incorporated in the algorithm.Each iteration of the algorithm is a very simple application of a small matrix inversion. Thissimplicity and dependence on the CMS software makes the HIPPY algorithm complementaryto MILLEPEDE-II.

Since our previous publications, the most important technical improvements in the HIPPY

alignment fit algorithm are:

1. The inclusion of alignment parameters beyond the three position and three orientationcoordinates of each sensor, namely the curvature of the sensors.

2. The possibility to apply a weight to certain types of input to balance statistical and sys-tematic uncertainties.

3. The option to perform sequential, hierarchical alignment over multiple time periods,when the time stability of the structures differs among the hierarchical levels.

4. The inclusion of possible mass and/or vertex constraints in certain types of events withknown physics process.

The diagram in Fig. 10 shows the design of the HIPPY algorithm with the sequence for theevent, track, and hit selection, including the application of the weight factors and constraints.The inclusion of the laser calibration data [1, 17] was developed during Run 1 but the lasersystem was not supported anymore in Run 2. The optical survey data constraint was usedduring the Run 1 start-up [1] and was used as a constraint to a prior geometry during theRun 2 start-up. The main new features of the HIPPY algorithm discussed above are indicated inthe diagram. These are the multi-IOV reconstructed track data, the sensor surface deformationdatabase object in addition to the sensor position, the division of input track data into categories

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Figure 10: Diagram of the HIPPY algorithm design with the sequence for event, track, and hitselection, including application of the weight factors and constraints. Not all features wereused in the Run 2 alignment procedure, as described in the text. The algorithm operates initerative mode, indicated with the arrows.

7 Alignment during data takingThe changes observed in the tracker require it to be realigned several times during the year. Inthis section, we focus on the two typical cases:

1. Restarting the detector with limited statistical power after a technical shutdown; we dis-cuss the beginning of Run 2 (Section 7.1.1) and the Phase-1 upgrade [29] (Section 7.1.2).

2. Running an automated, unsupervised alignment during data taking, with limited de-grees of freedom, illustrated for the period 2016–2018 (Section 7.2).

7.1 Start-up alignments

The general strategy during the start-up periods has been to run a series of alignment fits,where each time the starting point for the alignment fit is the set of alignment constants ob-tained in the previous fit. In case of large misalignments in the starting geometry, the lin-earization approximation made in the alignment algorithms is not valid anymore, and severaliterations are required to achieve convergence. To further simplify the alignment problem,we usually only gradually increase the number of degrees of freedom of the fit. This meansthe high-level mechanical structures are aligned first and the individual modules are alignedin a later iteration. In addition to the convergence aspects, this strategy also allows for earlyalignments with a small number of recorded tracks that do not sufficiently cover all modules,and would therefore be insufficient for a full module-level alignment. Thus, a continuous im-provement of the alignment precision can be achieved during the start-up period as data are

7.1 Start-up alignments 21

collected. In view of the potentially large misalignments to be expected during the start-upperiod, a particular effort has been made to run the HIPPY and MILLEPEDE II alignment algo-rithms independently to cross-check each other. In some cases, the two algorithms have alsobeen run successively to refine the alignment. For a given alignment task, the performance ofthe different available results was compared. The alignment with the best performance wasselected, and is presented in the following.

The first alignment constants after the extended shutdowns during Run 2 were derived aheadof the pp collision run, using exclusively cosmic ray muon tracks recorded by CMS duringcommissioning runs. This corrected for the large misalignments that are often observed afterthe extraction and subsequent reinstallation of the pixel detector. To retain tracking efficiency inview of potentially large misalignments, large alignment parameter uncertainties (APUs) wereassumed for the track reconstruction and fit. This is affordable only at very low event rates,which is the case during cosmic data taking. A further refinement of the alignments derivedwith only cosmic ray muon tracks was performed after recording a sufficiently large sample ofcollision tracks to be able to derive the alignment corrections with a higher granularity. In thefollowing, the alignment strategies and their performance with early 2015 and 2017 data arediscussed.

7.1.1 Start-up of Run 2

During the shutdown period between Runs 1 and 2 of the LHC, extensive maintenance andrepair work was performed on the tracking detector. The pixel tracker was removed to replacethe beam pipe. The pixel detector half barrel on the +x side was repaired, including replace-ments of several modules. During reinstallation, the barrel pixel detector was centred aroundthe beam pipe by displacing it upward by 3.4 mm and horizontally by 1.3 mm relative to itsprevious position. The strip detector was kept in place, but the cooling system was partiallyreplaced.

Initially, the latest available geometry of the Run 1 detector was used to reconstruct the data.An observed asymmetry in the track rate in the forward pixel endcaps quickly invalidated theassumed alignment constants and provided hints of a large initial misalignment of the FPIX.This was confirmed by the first alignment constants derived using 0 T cosmic data to determinethe relative positions and rotations of the high-level mechanical structures of the pixel and stripdetectors. The several million cosmic ray muon tracks collected at 3.8 T were used to furtherimprove the alignment configuration up to the level of single modules, by performing severalsuccessive alignment fits with an increasing number of degrees of freedom. Figure 11 showsthe shifts, relative to the Run 1 geometry, of the pixel module positions measured after themodule-level alignment fit. The BPIX as a whole was subject to a 3–3.5 mm shift, attributed tothe aforementioned recentring procedure of the BPIX around the beam pipe during the reinstal-lation. In addition, a relative shift between the two half barrel structures−π/2 < φ < π/2 andφ < −π/2, φ > π/2 was visible, which is attributed to the extensive repair and replacementwork in the −π/2 < φ < π/2 half barrel. The FPIX half disks on the −z side were displacedby −4.5 mm (φ < −π/2, φ > π/2) and −5.5 mm (−π/2 < φ < π/2) compared with the Run1 position. Much smaller relative movements of up to 200 µm were observed between the halfdisks on the +z side.

An example of the early alignment performance is depicted in Fig. 12, showing the distribu-tions of median track-hit residuals (DMRs) per module for BPIX and TIB modules. To avoidbiasing the measurement, the track is refitted removing the hit under consideration. The DMRsrepresent a measurement of the local alignment precision based on data. In the case of perfect

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Figure 11: Comparison of the positions of the modules in the first IOV of Run 2 and the last IOVof Run 1 in the BPIX (left) and FPIX (right) detectors, determined using cosmic data collectedwith 0 T and 3.8 T magnetic field in the solenoid and collision data at 0 T [30]. The differencesin position ∆y (Run 2 − Run 1) and ∆z (Run 2 − Run 1) of the sensor modules are shown as afunction of φ in global coordinates. Modules on the −z side are shown in red, modules on the+z side are shown in black.

alignment, the distribution is expected to be centred at zero. The width of the DMR is influ-enced by the residual random misalignment of the modules, as well as by an intrinsic statisticalcomponent due to the number of tracks used to construct the distribution itself. For this rea-son, the DMRs for two different sets of alignment constants can be compared only if they areobtained with the same number of tracks.

The local alignment precision of the pixel and the strip modules achieved in Run 2 with the fitusing the cosmic data is compared to the one obtained for the Run 1 geometry. The latter is notexpected to describe the detector well because of movements during the shutdown period, butwas the best available geometry description before realignment (Fig. 12, top row). Althoughthe alignment corrections for the strip detector were smaller than for the pixel detector, the mis-alignment of the pixel tracker had a noticeable effect on the performance of the strip detectorby reducing the accuracy of the tracks.

The track performance is also measured using the cosmic ray muon track split validation, as de-scribed in Section 5. The differences between the transverse and longitudinal track impact pa-rameters of the two track halves are shown in Fig. 13. The observed precision using the alignedgeometry comes close to that expected from the simulation for the case of perfect alignment.

Owing to problems with the cooling system, the superconducting CMS magnet was switchedoff shortly before the first collisions. As a consequence, the tracker geometry changed betweenthe CRAFT data and the first collisions, as can be seen by a widening of the DMR evaluatedwith the 0 T collision data in Fig. 12 (bottom row, green line). Because of the way the mechanicalstructures are mounted, a change of the magnetic field generally moves all of the high-levelstructures of the pixel detector, particularly in the global z direction. The relative positionsof the individual modules remain mostly unchanged. As expected, these effects are mainlyapparent in the pixel detector, which is installed on wheels and has some freedom to move


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Figure 12: DMRs for the local x coordinate in the BPIX (left) and TIB (right), evaluated using2× 106 cosmic ray muon tracks collected at 3.8 T (top) and 1.8× 106 tracks from collision data at0 T (bottom). The alignment constants used to fit the tracks were determined successively fromcosmic data at 3.8 T (green line) and, after the magnet was switched off, from 0 T cosmics andcollision data (black line) as described in the text. Because of the detector movements causedby the change in the magnetic field, the alignment constants derived with 3.8 T data (green line)are not optimal for the track fits in the 0 T data (bottom row). The blue line shows the DMRcomputed assuming the Run 1 geometry, which is no longer valid for Run 2 data.

relative to the rest of the detector, whereas the strip tracker position is relatively stable againstmagnetic field changes.

Starting from the alignment constants obtained using the 3.8 T cosmic data that produced thegreen line in Fig. 12, a new set of alignment constants was derived using the first 0 T collisiondata and cosmic data taken between collision runs. The pixel detector was aligned at the levelof single modules using a large sample of collision tracks, whereas for the strip detector onlythe positions of the high-level structures were updated. A module-level alignment fit of the full

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Figure 13: Distribution of the difference between two halves of a cosmic ray muon track, scaledby a factor

√2 to account for the two independent measurements. The track is split at the point

of closest approach to the interaction region, in the x-y (left) and z (right) distance between thetrack and the origin. The tracks are fit using the alignment constants determined with cosmicsat 0 T and 3.8 T (green circles) and using the Run 1 geometry (blue squares), which is no longervalid for Run 2 data. Vertical error bars represent the statistical uncertainty due to the limitednumber of tracks; they are smaller than the marker size. For comparison, the case of perfectalignment and calibration obtained from simulated events is shown (red line) [30].

detector with only 0 T collision data would have been vulnerable to WMs, e.g. to radial expan-sions of the entire detector, given the radial symmetry of the tracks. The fixed position of thestrip modules relative to their high-level structures provided a reference system. Furthermore,the addition of cosmic ray muon tracks, with their different topology, helped to avoid WMs.

The new alignment procedure improves the tracker performance, which is apparent from thenarrower DMR seen in Fig. 12 (bottom row, black line). The alignment precision is limited bythe poorer track resolution at 0 T, which is caused by the poor description of multiple-scatteringeffects at 0 T, because the track momentum cannot be measured in the absence of a magneticfield.

The alignment quality is also measured by its effect on the physics object performance. Inparticular, the reconstruction of the primary vertex (PV), e.g. the vertex belonging to the trackwith the highest pT, is driven by the pixel detector since it is the detector closest to the in-teraction point and has the best intrinsic hit position resolution. The unbiased track-vertexresiduals (track impact parameters) provide a measurement of the vertex reconstruction per-formance based on data. For each track, the PV position is reconstructed, excluding the trackunder scrutiny. A deterministic annealing clustering algorithm is used to make the methodrobust against pileup [7, 31]. The mean values of the distributions of the unbiased track-vertexresiduals are shown in Fig. 14. In the case of perfect alignment and calibration, mean valuesof zero are expected. Random misalignments of the modules affect only the resolution of theunbiased track-vertex residuals, increasing the width of the distributions without biasing theirmean. Systematic misalignments of the modules, however, bias the distributions in a way thatdepends on the nature and size of the misalignment. The structures of the green curve, whichis obtained when fitting the tracks with the alignment constants derived during the commis-


sioning phase with cosmic data at 3.8 T, indicate relative movements of the half barrels of thepixel detector during the decrease of the magnetic field. A clear improvement of the vertexperformance is observed for the subsequent alignment with 0 T collision data (black curve).Residual biases are attributed to suboptimal coverage of modules due to the limited number oftracks and their incidence angle, in addition to the aforementioned WMs and generally poorertrack resolution at 0 T.

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collision events collected at 0 T magnetic field. Two different alignments are used to fit thetracks: the alignment constants obtained during the commissioning phase with cosmic raymuon tracks at 3.8 T prior to collision data taking (green circles) and the alignment constantsdetermined subsequently with 0 T collision data (black squares). For comparison, the case ofperfect alignment and calibration obtained from simulated data is shown (red line). Verticalerror bars represent the statistical uncertainty due to the limited number of tracks; for the data,they are smaller than the size of the markers [30].

The tracker geometry changed again when the magnetic field was turned back on. Immediatelyafter the first 3.8 T collision data were taken, a fast alignment fit was performed with a limitednumber of tracks, updating only the positions of the pixel tracker high-level structures. Aftercollecting a larger data set, a full module-level alignment fit of the entire tracking detector wasperformed. This also included the available updates of the hit position calibration and furtherimproved the tracking performance.

7.1.2 Phase-1 upgrade

During the extended YETS starting at the end of 2016, the innermost component of the silicontracker was replaced with a new upgraded pixel detector [4]. The first data with the new pixeldetector were recorded in spring 2017, prior to the restart of the LHC pp run. After the firstperiod of detector commissioning to derive the initial calibrations, CRUZET and CRAFT datawere collected for alignment purposes.

First, only the forward pixel subsystem was included in data taking. Before performing anyalignment fit, the asymmetric track rate that was observed between the two FPIX endcaps al-ready provided a hint of a large initial misalignment for this subdetector. The first alignment fit

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of the FPIX high-level structures, namely the four half cylinders, was performed using 1.5× 106

reconstructed cosmic ray muon tracks collected at 0 T. The high-level structures of the strip de-tector were included in the alignment procedure as well, to avoid introducing any bias in thealignment constants for the pixel detector due to misalignment in the strip tracker. For thefirst track-based alignment after the installation, APUs of 500 µm and 20 µm for the pixel andstrip modules, respectively, were used in the track reconstruction and fit. The largest measuredcorrection to the assumed geometry was a shift of around 2.8 mm of the −z endcap of the for-ward pixel detector in the longitudinal direction, further away from the interaction point, asshown in Fig. 15 (left). Correcting this large misalignment eliminated the asymmetric trackrate between the two forward pixel sides. The strip tracker was not substantially misaligned.The magnitude of the alignment corrections was typically 10 µm for most of the substructures,with the exception of the tracker endcaps, which moved by around 100 µm in the longitudinaldirection.

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Figure 15: Corrections after the first high-level structures alignment of the pixel detector in2017, for the three FPIX disks (left) and the four BPIX layers (right). Shown are the differencesof the module positions after the module-level alignment of the pixel detector, with respect tothe ones considered before performing any alignment, as a function of the design positions.The alignments were performed with 0 T cosmic ray muon tracks.

Later, the barrel pixel detector was also included in data taking, and alignment correctionsfor the positions and orientations of the two half barrels were derived. This alignment usedaround 50 000 cosmic ray muon tracks, recorded at 0 T. The largest geometry correction was ashift of around 2 mm in the x direction of the whole barrel, as shown in Fig. 15 (right). Since thedetector was also rotated with respect to the geometry assumed before alignment, an increasingspread of ∆x with the radius r is visible.

The module-level alignment of the pixel detector was performed using the full data set col-lected during the CRUZET run, with around 3.2× 106 tracks used for the alignment. For thepixel modules, the APUs were reduced to 100 µm. The distributions of the unbiased track-hitresiduals in the local x and y coordinates for the BPIX and FPIX are shown in Fig. 16, for thetwo alignment iterations and for the initial geometry assumed before performing any align-ment. After each alignment iteration, the bias and the width of the residual distributions de-crease, demonstrating the reduction of systematic misalignment effects and an improved localprecision.

The improvements achieved by the different alignment iterations are visible in the cosmic raymuon track split validation as well. Figure 17 shows the differences in the η and φ parame-ters of the tracks at the different stages of the pixel detector realignment. The improvementachieved after each alignment iteration can be observed, since both the bias and the width of


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2 to account for the two independent measurements. The black circles show the geometryassumed before performing any alignment, the triangles show the FPIX and BPIX high-level(HL) structures alignment, and the red squares represent the module-level (ML) alignment ofthe pixel detector. The mean of the distributions (µ) is given in the legend. Vertical error barsrepresent the statistical uncertainty; they are smaller than the marker size in most of the cases.

An additional alignment was performed after the CMS magnetic field was turned on, usingthe tracks recorded in around 300 000 CRAFT events. The alignment constants needed to berederived to account for movements of the mechanical structures induced by the magnet ramp-up, causing biases in the distributions for the 0 T alignment fit. This is visible in the track-hitresidual distributions, as well as in the cosmic ray muon track split validation shown in Fig. 18(red squares). Despite the limited size of the data set, corrections for the strip and pixel detectorhigh-level structures were derived. These corrections removed the observed biases as shownin Fig. 18 (green crosses).

7.2 Automated alignment

During data taking the different components of the pixel detector may shift because of changesin the magnetic field or the temperature. To account for these shifts, an automated alignmentprocedure was implemented for Run 2 so that fast updates of the alignment parameters canbe provided within 48 hours. This alignment procedure runs as part of the prompt calibrationloop (PCL) [32], which processes several routines to control and automatically updates differentdetector-related parameters.

The alignment routine itself is based on a total of 36 degrees of freedom to account for themovement of the high-level structures in the pixel detector, namely two half barrels and twohalf cylinders in each of the two endcaps. For each of these structures, corrections for the po-sitions (x, y, z) as well as the rotations (θx, θy, θz) are derived using the MILLEPEDE-II alignmentalgorithm. The alignment is performed using tracks from the inclusive L1 trigger data set. Ifthe position of a structure changes by 5, 10, or 15 µm in the x, y, or z direction, respectively, orrotates by 30 µrad, the alignment parameters are updated for the prompt reconstruction of the

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next run. Figure 19 shows the results of the alignment routine for an arbitrary run in the 2016data set. For this run, the deployment of a new set of alignment constants was triggered by themovement of one of the endcap half cylinders in the z direction.

Figure 20 shows the movement of the two BPIX half cylinders in the x direction as a functionof the delivered integrated luminosity during Run 2. In general, a stable performance of theautomated alignment fit was observed over the full range of Run 2. Compared with 2017, a fewlarger movements can be observed in 2018 related to the presence of residual misalignments notcovered by the degrees of freedom used in the alignment during data taking. The beginningof data taking in each of the three years shows a period where the automated deploymentof the alignment constants was not active. In 2016 and 2017 these periods show only smallmovements. The movements in the corresponding period of 2018 are at a stable high level,indicating that the alignment was not corrected by an automated update.

During the transition between 2017 and 2018, a few detector modules of layer 1 of the BPIXwere replaced. The entire pixel detector was opened for this procedure. This resulted in largemovements in the detector, as the PCL alignment correctly indicated, and a manual alignmentprocedure was performed. Figure 21 shows the movements of all the individual pixel modulesduring this transition period. Large movements in the entire pixel detector, in addition tothe relative movements of the replaced parts, are clearly visible in this figure. In addition tothe alignment automatically derived in the PCL, manually derived alignments are also usedduring data taking. These alignments are based on a higher granularity. This is necessary,for example, after updates of the pixel detector calibration, as indicated in Fig. 20. Unlike thePCL alignment procedure, which is based on high-level structures, the higher granularity ofthe additional alignment fits further enables the correction of the radiation effects introducedin Section 3.

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Figure 19: Observed movements of the six high-level structures in the pixel detector from thealignment procedure in the PCL for one arbitrary run in 2016. The two horizontal red linesin each of the figures show the threshold for triggering the deployment of a new set of align-ment constants. The error bars represent the statistical uncertainties of the measurements. Theorange bars in the figure showing the movement in the z direction indicate that a sufficientmovement to deploy a new set of alignment constants was observed.

The distribution of the movement of the two BPIX half cylinders in the x direction, whichcorresponds to the projection of Fig. 20, is shown in Fig. 22. For both of the half cylinders, themajority of the runs show movements within the given thresholds and, therefore, do not triggeran update of the alignment. In both figures, the larger movements at the beginning of 2018discussed above are visible as smaller clusters. Without including runs where the deploymentof the new alignment constants was inactive, a total of 13–18% of the runs yielded a sufficientmovement in the x direction of one of the half cylinders to trigger an alignment update.

The necessity, as well as the success, of the automated alignment during data taking is alsovisible in Fig. 23. Here, for a short period in 2016, where the magnetic field was changedtwice, the movement in the x direction of all six high-level structures is shown for each runthat triggered an update of the alignment parameters. In both cases, large movements wereobserved in the first run after the change in the magnetic field. These movements triggered anupdate of the alignment, which was able to correct for the changes introduced by the magneticfield.

8 Alignment for physics analysisThis section describes the determination of the alignment constants in view of performingphysics analyses with the data taken from 2016 to 2018.

The derivation of alignment constants for physics analysis requires large samples to determinethe positions, orientations, and surface deformations of all sensors in the pixel and strip detec-

8.1 General strategy 31

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Figure 20: Observed movements in the x direction of the two BPIX half cylinders, as functionsof the delivered integrated luminosity, from the alignment procedure in the PCL. Each pointcorresponds to a single run. The vertical bars on each point represent the statistical uncertaintyof the measurement. The vertical black solid lines indicate the first processed runs for the 2016,2017, and 2018 data-taking periods, respectively. The vertical dashed lines illustrate updates ofthe pixel detector calibration. The two horizontal lines show the threshold for triggering thedeployment of a new set of alignment constants. The grey bands at the beginning of each yearindicate runs where the automated alignment updates were not active.

tors. Therefore, it is typically performed in the middle or at the end of the year. This is referredto as the “end-of-year (EOY) reconstruction” and it is shown in red in the figures in this section.In addition, after the completion of Run 2, a new set of alignment constants was derived forthe three years. This is referred to as “legacy reprocessing” and is shown in green in the figuresin this section. Throughout this section, we will describe how we have obtained these two newsets of alignment constants and compare them with the set of alignment constants used duringdata taking. This is labelled “alignment during data taking” and is shown in blue in the figuresin this section.

One important feature discussed in this section is the impact of radiation on the hit reconstruc-tion and consequently on the alignment procedure. This, together with the aim of controllingpotential WMs, drives the strategy.

First we will describe the strategy that was employed to account for the time dependence. Thenwe will compare the performance of the EOY reconstruction and of the legacy reprocessingwith the performance of the alignment during data taking. Finally, we will discuss specialdata-taking periods, such as runs with low pileup, runs at a centre-of-mass energy of 5.02 TeV,and heavy ion (HI) runs.

8.1 General strategy

Because of the large changes that occur during a YETS, as illustrated in Fig. 21, each data-takingyear is aligned separately. To maximize the statistical power of the cosmic ray muon track anddimuon resonance data sets, and to prevent systematic distortions from arising, the data col-lected during an entire year are combined to perform the alignment fit. Temporal changeswithin a year are taken into account by introducing a hierarchy in the alignment fit. The posi-

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tions of certain sets of modules are aligned over short periods of data taking corresponding toan integrated luminosity of around 1 fb−1 (a few days); such sets of modules can correspond tomechanical structures, since a whole mechanical structure can move coherently. The positions,

8.1 General strategy 33

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orientations, and surface deformations of all the sensors are aligned relative to these high-levelalignables.

During Run 1, the high-level alignables usually corresponded to the high-level structures. Dur-ing Run 2 this was still true for the strip detector, but smaller mechanical structures were cho-sen in the pixel detector to absorb the effects of radiation damage accumulating over time. Thisstrategy was applied for the first time in 2016, where ladders and blades, rather than the high-level structures, were treated as the high-level alignables. In 2018, with the increased levelof radiation, a better performance was obtained by considering ladders in the BPIX and themodules in the FPIX as the high-level parameters.

The IOVs for each set of alignment constants are determined from several sources:

• magnet cycles and known changes of temperature;

• changes in the hit reconstruction (including changes of the local calibration, changesof voltage, and ageing due to the irradiation);

• changes to the distributions of the impact parameters in the transverse plane 〈dxy〉and on the longitudinal axis 〈dz〉, as a function of the track angular variables η andφ (as introduced in Section 7.1.1), observed by eye on a per-run basis (typically cor-responding to 1–100 pb−1).

These last types of changes correspond, for example, to steps in the distributions matchingwith the geometrical coverage of the high-level structures (e.g. half-barrels in the BPIX). Suchchanges can also correspond to a scattering of the points as a function of φ, matching with theposition of the ladders.

In addition to the IOV boundaries, the level of precision of the alignment must be configured.Depending on the required precision and on the available data set, one can include more orfewer alignables, i.e. release more or fewer degrees of freedom. Moreover, the alignment pro-cedure and its physics validation are both computationally demanding, and only a limitednumber of configurations can be run and compared at a time. As discussed in Section 6, thesingle matrix inversion necessary for each tested configuration can take up to a full day of

34

pp collisions (16 August - 5 December 2016)

Figure 23: Observed movements in the x direction of the six high-level structures, as functionsof the run number from the alignment procedure in the PCL for the data-taking period between16 August and 5 December 2016. Each point corresponds to a run that triggered an update ofthe alignment parameters caused by a sufficient change in one of the three positions or rota-tions. The movements are shown without uncertainties. The vertical dashed lines illustrate thedeployments of new sets of alignment constants. The two horizontal lines show the thresholdfor a new alignment to be triggered. The grey shaded regions indicate runs during which themagnet was not at 3.8 T (magnet cycle). After each of the two magnet cycles a large movementis observed for the very first run after the cycle.

computing time. Similarly, the physics validation may take several days. Different configura-tions are attempted, and the configuration that provides the best physics performance is thenselected. As a result of the time involved, the number of attempts to understand the alignmentis sometimes limited. This was especially the case for the EOY reconstruction. However, forthe legacy reprocessing, the investigations spanned up to a few months. The calibration of thepixel detector local reconstruction was also refined, leading to a better physics performance,as will be illustrated throughout this section: we first investigate the tracking and vertexingperformance (Sections 8.2.1-8.2.2), then the presence of systematic distortions (Sections 8.2.3–8.2.5). The derivation of a final set of alignment constants for one data-taking year takes severalweeks, and involves two to five people for running the programs and comparing the differentconfigurations.

For the 2016 EOY reconstruction, the IOV boundaries were the same in the whole tracker. Lad-ders and blades were used as high-level alignables in the pixel detector. A global fit was firstperformed with MILLEPEDE-II, and was further refined with HIPPY using the same IOV bound-aries. For the 2017 EOY reconstruction, the alignment constants were derived independentlyusing either HIPPY or MILLEPEDE-II, but without time dependence within each period. For the2018 mid-year reconstruction (labelled ‘EOY reconstruction’ in the figures), only MILLEPEDE-IIwas used. Ladders and blades were used as high-level alignables in the pixel detector. In ad-dition, the alignment fit was performed in two steps: first the entire tracker was aligned using∼ 10 IOVs, corresponding to important changes. Then, the module parameters of the stripdetector were fixed and only the module parameters of the pixel detector were fit using ∼ 80IOVs. Note that there is no EOY reconstruction for the data corresponding to the last 33 fb−1

of the delivered integrated luminosity, because the derivation of the alignment for the legacy

8.2 Performance 35

reprocessing started at the end of the year 2018, before the end of the data taking.

For the legacy reprocessing, MILLEPEDE-II was used exclusively. The definitions of the IOVsand of the alignables were unchanged for 2016 (Phase 0) with respect to the EOY alignmentconstants. For 2017 and 2018 (Phase 1), different high-level alignables were chosen for thepixel detector: ladders were used in BPIX and modules in FPIX. In addition, different IOVboundaries were chosen in the pixel and strip detectors. Additional IOVs were introduced inthe pixel detector to absorb the bias from the hit reconstruction in the alignment constants.Adapting the strategy based on the results of the studies described in Section 5, systematicdeformations were avoided or reduced, except for the 2018 data-taking year. There, in additionto performing the alignment fit with MILLEPEDE-II, as the obtained alignment fit seemed tocontain a residual but significant twist deformation, an inverted twist transformation was alsoapplied to the geometry to compensate for this systematic distortion. This is shown in Fig. 9.

The different years required different strategies to obtain adequate performance for variousreasons. First, the different phases of the pixel detector imply different levels of precision. Thisleads to the observation of different tensions in the alignment. With more modules and beingcloser to the interaction point, the Phase-1 pixel detector was more demanding in terms ofalignment precision and suffered more from the effects of radiation than the Phase-0 detector.Similarly, the alignment for 2018 was more demanding than for 2017, likely because of theincrease in instantaneous luminosity and therefore radiation levels.

8.2 Performance

In this subsection, the performance for different sets of alignment constants in different data-taking periods is illustrated. The validation procedures already introduced in the text wererepeated to compare the quality of the different sets of alignment parameters. In addition, thestability of the performance is investigated as a function of the integrated luminosity deliveredby the LHC per run or per IOV. This is helpful to understand the ageing of the modules withthe accumulation of radiation effects.

8.2.1 Tracking performance

Figure 24 shows DMRs (introduced in Section 7.1.1) along the local x coordinate of the modulesbelonging either to the BPIX or to the TID. These distributions are produced with the single-muon data set instead of the L1 inclusive sample that is usually used for DMRs. This is doneto compare the performance in the data and simulated samples with similar event topologies,which is achieved by requiring exactly 100 hits per module in the simulation and in each IOV.Pixel detector (strip detector) modules with fewer than 100 hits per IOV over a period of datataking corresponding to an integrated luminosity of more than 2 (7) fb−1 were excluded fromthis study. The distributions are then averaged over all IOVs weighted by the correspondingdelivered integrated luminosity. A comparison is made to the realistic MC scenario derivedfor each year as well as the design MC scenario. These scenarios will both be introduced inSection 9. For an ideally aligned tracker, the DMRs are expected to peak at zero. This is visiblefor the MC simulation with the ideal alignment conditions, shown as the dashed magenta linein Fig. 24. A nonzero average may indicate a systematic shift of the structure under scrutiny.

The position of the pixel detector is known to be very sensitive to changes of the conditions,e.g. the temperature and the magnetic field. Furthermore, the width of the DMRs is determinedby several properties: the topology of the tracks; the number of hits per module, which isheld constant; and the quality of the alignment. To focus on the performance related to thealignment, the DMRs are calculated using the exact same sample of tracks in each case. The

36

mean and the width of the DMRs are extracted using a Gaussian fit. Figure 24 illustrates theimprovement of the performance with the large number of parameters in the global fit. Therewere more IOVs in the legacy reprocessing than in the EOY reconstruction, and smaller time-dependent structures were used. The improvement is largest for the legacy reprocessing.

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Figure 24: Distributions of per-module median track-hit residuals in the local x (x′) coordinate,for two subdetectors (BPIX and TID), produced with the single-muon data set. The distribu-tions are averaged over all IOVs, where each IOV is weighted with the corresponding deliveredintegrated luminosity. The DMRs are shown for three different geometries in data. They arecompared with the realistic MC scenario (black line) and the design MC scenario (magenta)evaluated in simulated isolated-muon events. The quoted means µ and standard deviations σare the parameters of a Gaussian fit to the distributions.

After the dedicated alignment for the Run 2 legacy reprocessing, the mean value µ of the dis-tribution of median residuals is shifted closer to zero and the mean difference shows improvedstability. The top panel of Fig. 25 shows µ extracted for the three different geometries, evalu-ated with a sample of data recorded by the inclusive L1 trigger, for each IOV as a function ofthe delivered integrated luminosity.

Because the direction of the Lorentz drift depends on the orientation of the modules, we alsoproduce DMRs in each subdetector for the inward- and outward-pointing modules separately,and calculate the differences between the means of the DMRs, ∆µ. A varying value is a hint ofresidual biases due to the accumulated effects from radiation in the silicon sensors, as alreadyintroduced in Section 3. The bottom panel of Fig. 25 shows ∆µ extracted for the three differentgeometries for each IOV as a function of the delivered integrated luminosity. The finer gran-ularity of the time dependence in the legacy reprocessing reduces the bias, since the rapidlychanging shift from the local reconstruction can be absorbed in the position of the ladders andof the modules. The improvement observed in Fig. 24 with respect to the EOY reconstructionis also directly related to this feature.

This effect of the radiation damage is shown in Fig. 26, where two different IOVs of the legacyreprocessing are compared with one another by showing ∆φ for each module of the pixel detec-tor. The IOVs were chosen such that the local calibration of the pixel modules was constant inthe time interval between them. They correspond to the first and last set of alignment param-eters of the 5th pixel IOV in 2017, where the ∆µ from Fig. 25 indicates large radiation effects.

8.2 Performance 37

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Figure 25: The DMR trends for the years 2016–2018, as functions of the delivered integratedluminosity, evaluated with a sample of data recorded by the inclusive L1 trigger. The upperfigure shows the mean value of the distribution of median residuals for the local x (x′) coor-dinate in the BPIX detector. The lower figure shows the difference between the mean values∆µ obtained separately for the modules with the electric field pointing radially inwards oroutwards. This quantity is also shown in the x′ coordinate. The shaded band indicates onestandard deviation from the Gaussian fit of the corresponding DMR.

The legacy reprocessing includes more updates during this period, which means the effects ofaccumulating radiation damage can be absorbed in the alignment. This results in an alternatingpattern in the ladders, since the modules of neighbouring ladders have opposite orientations.This pattern is especially visible in layer 1 of the BPIX. It is less visible in the next layers, wherethe effect is reduced due to the distance and is folded in with other effects. Such other effectsinclude movements of the high-level structures, e.g. the half barrels of the BPIX.

The understanding of the interplay of the local reconstruction and of the alignment procedureis a great improvement in the physics performance in the legacy reprocessing.

8.2.2 Vertexing performance

The distributions showing the impact parameters of tracks were already introduced in Sec-tion 7.1.1. Figure 27 shows the average unbiased track-vertex distance along the beam axis 〈dz〉and in the transverse plane 〈dxy〉, as a function of the η and φ of the tracks. Only tracks thatsatisfy pT > 3 GeV are considered. The distributions are averaged over all runs and weighted

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Figure 26: The value of ∆φ for each module in the pixel detector, comparing the alignmentparameters of the legacy reprocessing on 3 and 10 August 2017 (left) and comparing the align-ment based on MC simulation for 2017 and the ideal detector (right). For each of the detectorcomponents, φ corresponds to the global coordinate. The colours denote the value of the ∆φmovement, as shown by the bar at the bottom. These values are capped between −0.15 and0.15 mrad for the alignments in data (left) and between −0.05 and 0.05 mrad for simulation(right). In the figure on the right, modules that were inactive in the simulation are indicated indark grey. The alternating pattern visible in layer 1 of the BPIX is caused by radiation damagethat is absorbed in the modules between local calibration updates of the pixel modules. Due tothe opposite orientations of the neighbouring ladders, an alternating pattern is created. Radi-ation damage is more severe in the first layer which is closer to the interaction point, makingthe pattern more visible.

with the corresponding delivered integrated luminosity per run, normalized to the total inte-grated luminosity delivered in 2016–2018. The results are shown for the three geometries andare compared with the realistic MC scenario derived for each year, which will be describedin Section 9. The distributions are expected to be flat and compatible with zero for an ideallyaligned tracker. The impact parameter as a function of φ shows some improvement with theEOY reconstruction, but the performance as a function of η is only improved with the legacyreprocessing. The remaining residual deviations from the ideal case observed in the legacyreprocessing at high absolute pseudorapidity can be explained by a limited tracking perfor-mance related to the pixel detector local reconstruction in the data. The deviation visible in thesimulation will be discussed in Section 9.

Similarly to the DMR trends, we also calculate impact parameter trends. Figure 28 shows the

8.2 Performance 39

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Figure 27: Mean track-vertex impact parameter in the transverse plane dxy (left) and in thelongitudinal plane dz (right), as a function of track φ (top) and η (bottom). The impact parame-ters are obtained by recalculating the vertex position after removal of the track under scrutinyand considering the impact parameter of this removed track. Only tracks with pT > 3 GeV areconsidered. These distributions are averaged over all runs of 2016, 2017, and 2018 after scalingthem with the corresponding delivered integrated luminosity for each run. Three alignment ge-ometries in data are compared with the realistic MC alignment scenario evaluated in a sampleof simulated inclusive L1 trigger events (black points) scaled to the corresponding luminositydelivered in 2016, 2017, and 2018. The error bars represent the statistical uncertainties due tothe limited number of tracks. In case of data points statistical uncertainties are smaller thansize of the displayed markers.

average and the spread of the distributions on a run-by-run basis, as a function of the deliveredintegrated luminosity for the three geometries. The suboptimal tracking performance duringthe first few inverse picobarns of the 2017 pp collision run corresponds to the commissioning ofthe upgraded pixel detector. This is visible in the degraded impact parameter bias around the

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thick vertical line, which indicates the Phase-1 upgrade. Apart from this short period, aligningthe tracker improves the mean of this distribution. Short IOVs with a suboptimal configurationof the pixel local reconstruction, e.g. different high-voltage settings or local reconstruction pa-rameters that are inconsistent with the alignment, can give rise to isolated peaks in the trends,especially for the alignment derived during data taking. The slopes in the RMS trend that arevisible between two pixel calibration updates for the alignment during data taking are dueto residual radiation effects not included in the dedicated calibration, e.g. at the beginning of2018. This causes the Lorentz drift to change rapidly. This residual effect can be corrected onlyby aligning with a finer granularity than the automated alignment implements. In general, theimproved performance obtained with the legacy reprocessing is also related to the more gran-ular configuration of the alignment fit in comparison with the alignment during data takingand for the EOY reconstruction. This is especially the case in 2017.


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To obtain information about the PV resolution, the set of tracks assigned to a vertex is splitinto two independent data sets. For this, instead of the usual inclusive L1 trigger sample, weuse a sample populated with tracks at high pT. The tracks are sorted according to their pT andassigned to a pair starting from the track with the highest pT. From each pair, one track is ran-domly placed into one of the new samples to ensure similar kinematic attributes. For both newdata sets, the adaptive vertex fitter is run and the residual of the PV resolution is calculated asthe difference between the results. This is done for several values of ∑ pT for all tracks in the

8.2 Performance 41


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respective data set, and the distribution of residuals is fitted with a Gaussian function. Thisscalar pT sum is used to categorize the vertices, because the resolution improves when a largeroverall pT is assigned to a vertex. The PV resolution is defined as the mean of the fitted Gaus-sian distribution. In Fig. 29, the resolution for all runs recorded in 2016, 2017, and 2018 is shownusing the three usual geometries, for the x and z vertex coordinates. As an illustration, two dif-ferent selections in terms of the minimum track pT sum are shown. The maximum resolutionfor each vertex coordinate is obtained for ∑ pT > 400 GeV, which is approximately 15% betterthan in the case of the looser selection ∑ pT > 200 GeV. The conclusions are similar to those ob-tained with the impact parameter trends: an improvement corresponding to the upgrade of thepixel detector is observed, especially in the z direction, and isolated peaks correspond to shortIOVs with a suboptimal configuration of the pixel local reconstruction. Although the EOY re-construction shows some improvement with respect to the alignment during data taking, thelegacy reprocessing seems to improve only the performance of certain outliers.

8.2.3 Dimuon invariant mass reconstruction

The dimuon mass validation using Z → µµ events was already discussed in Section 5. Beyondthe improvement of the local precision, another improvement brought by the legacy reprocess-ing is related to the systematic distortions, as demonstrated in Fig. 30. The sigmoid shape,

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typical of a twist distortion, is reduced in the legacy reprocessing when compared with thealignment during data taking and to the EOY reconstruction. Although the EOY reconstruc-tion of the 2016 data did not suffer from such a distortion, that of the 2017 and 2018 data didsuffer from it. For 2017, the distortion was improved by performing the alignment fit withMILLEPEDE-II in the legacy reprocessing. However, the distortion observed in 2018 was not re-moved by the same procedure and a twist transformation was applied in the opposite directionin addition to running MILLEPEDE-II for the legacy reprocessing. The reason for the need forextra processing in 2018 is not understood to date.

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Figure 30: Reconstructed Z boson mass as a function of the difference in η between the posi-tively and negatively charged muons, calculated from the full sample of dimuon events in theyears 2016, 2017, and 2018. The error bars show the standard deviation of the invariant Z bosonmass as retrieved from a fit of dimuon mass distribution to a Breit-Wigner convolved with aCrystal Ball function.

Further systematic distortions have been reduced with the legacy reprocessing. For instance,the bias in the reconstructed mass as a function of φ due to a systematic distortion is periodicto first order, and the distributions are easily fit with a cosine function. As such, the ampli-tude of the fitted cosine function is a good measure for quantifying the magnitude of the biasin the reconstructed dimuon mass. The top figure of Fig. 31 demonstrates the periodicity ofthe reconstructed Z boson mass, whereas the bottom figure of Fig. 31 shows the amplitudeof the reconstructed Z boson mass as a function of the delivered integrated luminosity. Theamplitude shows the average spread of the reconstructed Z boson mass with respect to φµ+

,which is expected to be zero in a well-aligned detector. A nonzero amplitude indicates thatthe reconstructed mass has some dependence on the spatial coordinates of the detector. Weobserve an improvement in the legacy reprocessing compared with the earlier alignment pro-cedures. However, the legacy reprocessing still shows a suboptimal performance in the 2018data-taking year in comparison with the two other years. In addition to this, from the point ofview of Fig. 31, the legacy reprocessing shows worse performance than the alignment duringdata taking, especially in the last 30 fb−1. This is suspected to be related to the discrepanciesbetween different IOVs with different configurations while operating the pixel detector, e.g. achange of voltage or annealing.

8.2 Performance 43

Alignm 2χ

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Figure 31: The upper figure shows the invariant mass of the dimuon system, as a function ofthe azimuthal angle of the positively charged track for a single IOV. The lower figure showsthe amplitude A, obtained by fitting the invariant mass of the dimuon system versus φµ+

witha function of the form A cos (φ + φ0) + b as a function of the delivered integrated luminosity.The vertical bars on the points in the upper figure show the uncertainty in the average mµµ

of a given φµ+bin. The shaded bands in the lower figure show the uncertainty in the fitted

parameters calculated by a χ2 regression.

44

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Figure 32: Performance results for cosmic ray muon tracks recorded during commissioningand interfill runs at 3.8 T during 2016, 2017, and 2018. The top and bottom halves of the cosmicray track are reconstructed independently and the track parameters are compared at the pointof closest approach to the interaction region. The mean and RMS of the distribution of ∆ηrelative to its uncertainty are shown in the figure on the left. The mean η difference betweenthe two tracks is presented as a function of dxy on the right, scaled down by

√2 to account for

the two independent measurements. The error bars show the statistical uncertainty related tothe limited number of tracks.

8.2.4 Cosmic ray muon track reconstruction

As was mentioned in Section 5, cosmic ray muon tracks are one of the key ingredients usedto control systematic distortions in the alignment procedure. The quality of the alignmentconstants, and in particular the presence of certain systematic distortions, can also be better as-sessed by studying the performance of the reconstruction of cosmic ray muon tracks. Figure 32shows ∆η and the difference in the impact parameter in the transverse plane between the twohalf tracks refitted from the hits of a cosmic ray muon traversing the detector.

From this perspective, the strategy followed in the legacy alignment procedure has led to betterperformance. In particular, the improvement of the distribution of ∆η relative to its uncertaintyis related to the improved statistical precision of the alignment fit. The reduction in the differ-ence between the impact parameters is related to the reduction of systematic distortions; inparticular, for the left figure, it can be interpreted as a reduction of the telescope WM.

8.2.5 Overlap validation

The overlap validation was already introduced in Section 5. Figure 33 shows the mean overlapresiduals as a function of the delivered integrated luminosity. The nonzero values, even forthe legacy reprocessing, illustrate the limitations of the strategy followed for the alignment fit.The biases that were introduced by the radiation damage are artificially absorbed in the align-ment constants. Correlations between shifts in the mean overlap in Fig. 33 and ∆µ in Fig. 25demonstrate that the mean overlap is also an effective measure of the Lorentz angle calibrationbias. Alternatively, if any observed deviations are assumed to be caused purely by system-atic distortions, constraints can be placed on the maximal magnitude of the distortions in thetracker. These constraints represent a worst-case misalignment scenario, where no biases are

8.2 Performance 45


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Figure 33: The upper (lower) figure shows the mean difference in z (φ) residuals for modulesoverlapping in the z (φ) direction in the BPIX, 〈∆δz〉 (〈∆(rδφ)〉), as a function of the deliveredintegrated luminosity. The error bars show the statistical uncertainty in the mean of distribu-tion of the residuals. These residuals are calculated using a sample of data recorded with theinclusive L1 trigger.

introduced by radiation damage. In the top row (bottom row) of Fig. 33, we observe deviationsfrom zero in the legacy reprocessing ranging from −21 µm to +2 µm (from −1.9 µm to +5 µm),which correspond to longitudinal (radial) expansions or contractions ranging from−105 µm to+10 µm at z = 26 cm (−5.7 µm to +16.5 µm at r = 16 cm).

8.2.6 Barycentre of the barrel pixel detector

The barycentre position is determined as the centre-of-gravity of the pixel modules, either con-sidering all pixel modules or barrel pixel modules only. The position of the BPIX barycentre isshown in Fig. 34 as a function of the delivered integrated luminosity. The barycentre position isextracted directly from the alignment parameters. The differences between the years illustratethe precision of the mechanical mounting of the detector. During the year, the position is con-stant within a few µm, especially for the legacy reprocessing. Large differences in the positionat the beginning of the 2017 and 2018 data-taking periods are caused by the extraction and rein-stallation of the pixel detector during the shutdowns. This was done for the Phase-1 upgrade in2017, and for module replacements in 2018. The alignment during data taking, which does notinclude corrections related to the accumulation of the radiation damage, introduces an artificialshift in the barycentre position. This shift is reduced in the legacy reprocessing, where we have

46


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Figure 34: The global y coordinate of the barycentre position of the barrel pixel detector asa function of the delivered integrated luminosity, determined as the centre-of-gravity of themodules in the barrel pixel detector only.

shown that the effects of radiation damage were more successfully included in the alignmentprocedure.

8.2.7 Alignment parameter uncertainties

The APUs account for the uncertainty in the positions of the modules derived from the align-ment fit. The difference between the real and measured module positions is equivalent to aresidual misalignment that causes a widening of the distributions of the track-hit residuals,which are given by r = m− f (p, q). Therefore, to estimate the contribution from the misalign-ment to the hit residual distribution, the normalized hit residual resolution σr is calculated.The resolution squared is given by the quadratic sum of the cluster position estimation (CPE)uncertainty and the uncertainty of the prediction by the track fit, excluding the hit under study:

σ2r = σ2

hit + σ2trk. (5)

The normalized hit residual distribution, rσr

, should have the same width as the design simula-tion for a perfectly aligned tracker, with a broader and shifted distribution in case of misalign-ment.

To compensate for the broadening of the hit residual distribution caused by the misalignment,an additional uncertainty σalign is introduced, which is added in quadrature to the initial reso-lution:

σ2r′ = σ2

r + σ2align. (6)

Since the additional alignment uncertainty is also included in the tracking covariance matrixand influences the hit association windows used in pattern recognition, σalign affects the trackreconstruction itself. This is included by calculating σalign iteratively, where the track candidateis refitted for each iteration, using the previous estimate of σalign. To ensure convergence, 15iterations are performed, which is sufficient.

To account for possible incorrectly estimated track fit uncertainties or deficits in the CPE pa-rameterization, the width of the normalized hit residual distribution is compared with valuesobtained from ideal simulation. The APUs are then determined in such a way that the width

8.2 Performance 47

of the distribution of r/σr′ matches that of the ideal simulation. If the normalized hit residualdistribution is not wider than the one for the ideal case, the final σalign value is set to zero.

The contribution from the misalignment of the sensors to the total hit resolution for the innerladders of the first and second BPIX layers in the local y coordinate is shown in Fig. 35 for datataken from 2016 to 2018, as a function of the delivered integrated luminosity. The alignmentuncertainty mainly varies between 0 and 30 µm and is only larger for the alignment during datataking at the beginning of data taking in 2017, because the pixel detector was replaced. For thelegacy reprocessing, the measurements were performed with a higher granularity. The legacymeasurements might have larger values compared with earlier reconstructions because of thehigher granularity and because a decrease of the CPE uncertainty can result in an increase ofσalign. The resolution of the second layer is better in comparison with the first layer because ofthe higher radiation dose in the first layer.


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Figure 35: The contribution from the misalignment of the sensors to the total hit resolution forthe inner ladders of the first (top) and second (bottom) BPIX layers in the local y coordinate as afunction of the delivered integrated luminosity. For the legacy reprocessing, the measurementswere performed with a higher granularity than in the other two cases.

The contribution from the misalignment of the sensors to the total hit resolution is shown inFig. 36 for the pixel detector and the inner barrel region of the strip detector. As an example,a data set recorded in October 2017 was selected as a representative sample showing the per-formance for the detector geometry in 2017 and 2018. The results obtained from the legacyreprocessing are more granular and yield smaller or similar alignment uncertainties than the

48

alignment uncertainties obtained during data taking or from the EOY reconstruction in mostmodule categories.

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Figure 36: The contribution from the misalignment of the sensors to the total hit resolution inthe local x coordinate for the tracker pixel detector (left) and the inner barrel region of the stripdetector (right). These contributions are shown separately for the different module categoriesthat characterize the hierarchical structure of each subdetector.

Finally, the change in the estimation of the alignment accuracy is also visible in Fig. 37, wherethe distribution of the RMS of the normalized track-hit residuals produced with the single-muon data set is shown for the BPIX and the TID. In this figure, the same high-level structures,and the same condition on the number of hits per module, as in Fig. 24 are used. The distribu-tions in data are averaged over all IOVs, weighted by the corresponding delivered integratedluminosity. These distributions are compared with the realistic and ideal single-muon MC sce-narios that will be introduced in Section 9. The improvement from the legacy reprocessingrelated to the finer granularity can also be seen, because the means of the distributions shiftcloser towards unity. The distributions are not expected to be centred exactly around unity,since an incorrect estimation of the track or hit uncertainty causes this centre to deviate fromunity. This can be observed in the ideal simulation without misalignment.

8.3 Special runs

The approach used to align the tracker during periods of data taking with lower pileup or adifferent centre-of-mass energy than usual, as well as during HI collisions, differs from thedescription in Section 8.1. These data sets are small compared with the standard pp collisiondata set and are used independently from it in physics analyses. Short periods of time duringwhich the alignment performance is suboptimal do not have a significant effect on the largestandard pp collision data set. For the much smaller data sets collected during special runs,a short period of suboptimal alignment performance would have a comparatively more im-portant impact. A dedicated alignment strategy is therefore employed to ensure the alignmentperformance is maintained in these subsets of the data.

8.3.1 Low-pileup runs and runs at 5.02 TeV

Several low-pileup runs were taken during Run 2. These runs had an average of three interac-tions per bunch crossing, whereas under normal operating conditions the average number ofinteractions per bunch crossing was greater than 20. In 2017 (2018) a data set correspondingto an integrated luminosity of 0.22 (0.03) fb−1 was collected under these low-pileup conditions.The low-pileup runs in 2017 were included in the global legacy alignment procedure described

8.3 Special runs 49

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Figure 37: The distribution of the RMS of the normalized residuals in the local x coordinate(x′) for modules in the BPIX (left) and in the TID (right). The distributions evaluated in dataare averaged over all IOVs, weighted by the integrated luminosity delivered in each IOV. Thedistributions in data are compared with an MC scenario with realistic alignment conditions forthe legacy reprocessing and an MC scenario with ideal alignment conditions. An improvementis visible in the legacy reprocessing compared with the other two alignments shown; this isquantified by the quoted means µ of the distributions.

in Section 8.1. To improve the performance of the alignment for the low-pileup runs, the resultof this global alignment was used as a starting point for an additional alignment fit. This laststep used only tracks from the low-pileup runs, and the positions of the pixel detector mod-ules were allowed to vary. The parameters of the strip detector remained fixed to the valuesdetermined in the first step.

In 2017, a data set corresponding to an integrated luminosity of 90 pb−1 was collected at acentre-of-mass energy of 5.02 TeV. An alignment fit was performed following the same ap-proach as for the low-pileup runs. However, the runs at 5.02 TeV were not included in theglobal legacy alignment procedure.

For the low-pileup runs taken during 2018, a different approach was employed. On top ofthe legacy alignment procedure, a fit using only tracks from the 2018 low-pileup runs was per-formed. In this step the parameters of the strip detector were fixed to the values obtained in theglobal procedure and the parameters of the half-barrels and half-cylinders of the pixel detectorwere allowed to vary. In this case the complexity of the second step is reduced compared withthat employed for the low-pileup runs taken in 2017 as a result of the much smaller amount ofdata collected.

8.3.2 Heavy ion runs

The track multiplicity in HI collisions, especially in central ones where the nuclei collide head-on, is much higher than in pp collisions. In general, it is not guaranteed that the techniquesdeveloped in the context of pp collision data apply directly to HI runs.

The initial alignment constants used for the HI running period were determined from pp runswith the same detector settings as used during the HI data-taking period. Movements in the

50

high-level structures of the pixel detector were observed by the PCL alignment procedure atthe beginning of HI data taking. The automated alignment procedure triggered by these move-ments improved the tracking and vertexing performance.

To further improve the performance, a module-level alignment fit was performed in the BPIXand FPIX using the HIPPY algorithm with 30 iterations. As an input for the alignment fit,approximately 100 000 events from a data set that preferably contained events where the twocolliding lead ions only partially overlap were used. The HIPPY alignment procedure wasvalidated by investigating the DMRs and impact parameters (presented in Section 7.1.1) forthe data with and without applying the obtained alignment conditions. An improvement of thetracking performance in the pixel detector was observed with the new alignment conditions,both in the barrel and in the forward regions.

No additional alignment fits were performed for these data sets as part of the legacy alignmentprocedure.

9 Alignment in simulationTo achieve the best possible modelling of the data, simulated events are processed through thesame detector reconstruction chain used for events in the observed data. This requires the fullset of calibrations, including tracker alignment constants, to be derived for the simulation aswell. The main purpose of such constants is to reproduce, as accurately as possible, the sameperformance and effects observed in the data. This section presents the strategy adopted toderive alignment conditions consistent with the ones for the legacy reprocessing. This specificcase is most interesting because the alignment constants for the simulation were derived afterthe final conditions for data reprocessing were available, thus making better tuning possible,as described in Section 9.1. The conditions are derived separately for each data-taking year,though no further time dependence is included in the simulation. The alignment scenarios forsimulation are validated using the same methods used for data. Therefore a direct comparisonof the quantities sensitive to alignment effects is possible, as shown in Section 9.2. The gooddescription of the data achieved by the dedicated simulation is also demonstrated in Section 9.3in terms of track-related quantities, such as the impact parameters and the χ2 distributions ofthe track fits.

9.1 Derivation of the alignment

The general strategy used to derive accurate alignment constants for the simulation relies onreproducing the procedure adopted for the data as closely as possible. For this, a full alignmentfit is performed using simulated events.

The starting geometry for the fit is built starting from the ideal detector geometry, with mis-alignments applied to reflect the average accuracy of the alignment constants in data after theEOY reconstruction. A Gaussian smearing is applied to the ideal module positions in the localx, y, and z directions. The RMS values for the x and y directions are obtained from the DMRdistributions of the EOY reconstruction. This is done separately for each tracker partition.When appropriate, further systematic shifts of the tracker substructures are also applied, ac-counting for the presence of systematic misalignments. Table 4 shows the shifts of the modulepositions applied to construct the scenarios used as a starting point for the MC fits for the 2016and 2017 legacy alignment conditions. Similarly, a smearing of 10 µm was applied to all trackersubstructures in the three local directions for the derivation of the 2018 legacy MC alignmentconditions.

9.2 Validation 51

Table 4: Magnitude of the Gaussian smearing applied to the design geometry to derive thestarting geometry for the 2016 and 2017 legacy MC alignments. The adopted values in bothyears are reported for each substructure and by coordinate.

Year Substructure σx [ µm ] σy [ µm ] σz [ µm ]

2016

BPIX 3.0 9.1 —FPIX 9.0 9.0 —TIB 4.8 4.8 —TOB 11.7 11.7 —TID 3.3 3.3 —TEC 6.9 6.9 —

2017

BPIX 6.1 17.0 5.0FPIX 5.3 2.7 5.0TIB 13.7 13.7 5.0TOB 30.9 30.9 5.0TID 6.3 6.3 5.0TEC 13.6 13.6 5.0

After having determined the starting point for the MC alignment, the fit is performed using thesame granularity as for the data. The set of simulated tracks used for the fit is obtained fromseveral samples, reflecting the variety of topologies used in data, as discussed in Section 4. Thecomposition of the sample used in the data alignment is closely matched using event weights.

9.2 Validation

The alignment constants derived from the fit are then validated and compared with the data.First, the tracker geometry obtained from the fit is compared with the ideal one to directlyassess the recovery from misalignment and spot any unusual movements or systematic distor-tions that might be artificially introduced by the fit. An example is provided in the right panelof Fig. 26, showing the differences in the module positions in the φ coordinate for the 2017MC alignment with respect to the ideal detector geometry. No unexpected large movements ormisalignments are observed, providing a first indication that the alignment fit performs well.Figure 38 shows the differences in the module positions in the z coordinate for the 2018 MCalignment. Here, movements characteristic of a z expansion WM are observed in the TEC.These movements are not expected to have a large impact on the alignment performance.

Since the performance in data is generally not uniform in time, three representative IOVs areselected and compared with the alignment in simulation. The impact parameter performancefor the 2017 MC scenario is shown in Fig. 39. An attempt to reproduce the modulations ob-served at high absolute pseudorapidity for the three selected IOVs was made by applying acoherent shift of 30 µm in the z coordinate to the endcaps of the FPIX. In a similar approach, a10 µm ladderwise alternation of the bias in the x coordinate was applied to the first layer of theBPIX in the 2018 MC scenario. The local precision of the MC alignment procedure, which canbe estimated from the DMR distributions and compared with the data, is shown in Fig. 40 for2017. The effect of the introduced systematic misalignment can be observed in the distributioncorresponding to the FPIX. On average, the performance of the MC alignment matches the per-formance observed in data. The same observation is supported by the results of the cosmic raymuon track split validation, shown in Fig. 41.

With this set of validations shown for the 2017 MC misalignment scenario, we conclude that thederived scenario is consistent with the conditions observed in the CMS detector. Therefore, it

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9.2 Validation 53

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Figure 39: Distribution of the mean impact parameter in the transverse plane (left) and in thelongitudinal plane (right) as a function of the track φ (top) and η (bottom) for 2017. The derivedMC scenario is compared with three representative IOVs from the year 2017 in data (18 July,18 August, 5 October) to assess its validity as the final geometry. The error bars show thestatistical uncertainty related to the limited number of tracks.

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Figure 40: The DMRs in the local x coordinate for different components of the tracker system.The derived MC scenario is compared with three representative IOVs from the year 2017 indata (18 July, 18 August, 5 October) to assess its validity as the final geometry.

9.3 Comparison of data and MC performance 55

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Figure 41: Difference in the impact parameters in the longitudinal (left) and transverse (right)plane as evaluated in the cosmic ray muon track splitting validation. The derived MC scenariois compared with three representative IOVs from the year 2017 in data (18 July, 18 August,5 October) to assess its validity as the final geometry.

was chosen as the alignment scenario for the 2017 legacy MC simulation. The same procedurewas applied for the other two years, and a final candidate consistent with the CMS detectorconditions was delivered.

Finally, the performance of the MC scenario for each year separately is shown in Fig. 42. Theperformance of the realistic MC scenario scaled to the corresponding delivered integrated lu-minosity was already shown in Fig. 27 to provide a comparison with the average performancein the three considered geometries. The observed residual deviations from the ideal case (meanof 0) should reflect the alignment precision and radiation effects in data, mostly visible for thehigh-|η| region, which cannot be fully fixed by the alignment. Apart from this region, a verysimilar performance was achieved for the simulation in each year.

9.3 Comparison of data and MC performance

A set of track properties is compared between data and MC simulation after the Run 2 legacyreprocessing to check the alignment of the detector. This is done to ensure the data and MCsimulation are compatible. In this study, inclusive L1 trigger pp collision data reconstructedwith the Run 2 legacy reprocessing were compared with a simulated sample of single-neutrinoproduction to produce events containing only pileup and detector noise. In early 2016, thesilicon strip detector observed a decrease in the signal-to-noise ratio, associated with a loss ofhits in tracks. This behaviour was caused by saturation effects [33] in the preamplifier circuitused in the strip tracker readout ASIC (APV25) chip [34]. During this period, a data set cor-responding to an integrated luminosity of 20 fb−1 was impacted by this effect. It was resolvedby optimizing the setting of the parameter governing the drain speed of the preamplifier in theAPV25 chip on 13th August 2016 to achieve a recovery of the hit efficiency to the same levelas in Run 1. The period affected by this problem is referred to as having old APV settings,whereas the 2016 data-taking period after the resolution of the issue is referred to as using newAPV settings. A legacy MC sample, containing a dedicated description of the APV25 chip dy-namic gain [35] was made for the first part of the 2016 data-taking period and is referred to as

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Figure 42: Mean track-vertex impact parameter in the transverse plane (left) and in the longi-tudinal plane (right), as a function of the track φ (top) and η (bottom). A comparison of theresults with the alignment constants derived in the Run 2 legacy MC scenario for 2016, 2017,and 2018 separately is shown. The error bars show the statistical uncertainty related to thelimited number of tracks.

“MC with APV simulation”.

The runs where important updates were made in the legacy reprocessing are used to check theperformance. The study uses 0.30 fb−1 of 2016 data with old APV settings, 0.28 fb−1 of 2016data with new APV settings, 0.12 fb−1 of 2017 data, and 0.53 fb−1 of 2018 data. To comparedata and simulation, the simulated events are reweighted with respect to the distribution ofthe number of vertices. In addition, the simulated samples are normalized to the integratedluminosity of the data. In general, “high-purity” tracks [7] are the default track selection formajority of physics analyses in CMS, unless efficiency is essential and purity is not a major con-cern. Therefore we consider only good “high-purity” tracks to maintain high efficiency (with atypical efficiency of more than 95%) along with the rejection of a large number of misidentifiedtracks (the misidentification rate is less than 8%). Three track observables that are sensitive


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Figure 44: The distribution of the uncertainty in measuring the three-dimensional track impactparameter (IP3D), with respect to the PV. Data collected in 2016 (left) and 2017 (right) are com-pared with the corresponding MC simulation. For 2016, the simulation with old APV settingsis shown. For both data and simulation, the last bin includes the overflow. Vertical error barsrepresent the statistical uncertainty due to the limited number of tracks; they are smaller thanthe marker size in both years.

to the tracker alignment were used to compare the data and the simulation. For all of theyears, comparable agreement between the data and MC simulation was observed. During the2016 YETS, the Phase-1 upgrade of the pixel detector [3, 4] took place. To compare data and

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Figure 45: Distribution of the track χ2 probability for the number of degrees of freedom in thetrack fit. Data collected in 2016 (left) and 2017 (right) are compared with the correspondingMC simulation. For 2016, the simulation with old APV settings is shown. Vertical error barsrepresent the statistical uncertainty due to the limited number of tracks; they are smaller thanthe marker size in both years.

MC simulation for the Phase-0 detector, a study of 2016 data and MC simulation using oldAPV settings, and with a dedicated description corresponding to the APV dynamic gain, wasperformed. The same was done with 2017 data and MC simulation to make the equivalentcomparison for the Phase-1 detector.

The two-dimensional track impact parameter significance (SIP2D) with respect to the PV inthe transverse plane of the detector is defined as the ratio of the two-dimensional track impactparameter with respect to the PV and its uncertainty. Track impact parameter uncertainties arecalculated from the covariance matrix of the fitted track trajectory. A comparison between dataand MC simulation of the distribution of the SIP2D with respect to the PV is shown in Fig. 43for the legacy reprocessing of the 2016 and 2017 data. The symmetric nature of the figures in-dicates the detector is aligned well in both years. In 2016, the agreement between data and MCsimulation becomes worse for higher values of SIP2D. This discrepancy is not due to misalign-ment, but is instead caused by the somewhat imperfect simulation of the APV saturation in thecase where strip hits contribute more to the tracks in the tails of the distribution. In 2017, goodagreement between data and simulation is seen.

The IP3D uncertainty is the uncertainty in measuring the three-dimensional track impact pa-rameter. The distribution of the IP3D uncertainty with respect to the PV is shown in Fig. 44,comparing data and simulation. In the 2016 data-taking period with the old APV settings, anotable deviation between data and MC simulation is seen for large values of the IP3D un-certainty. This deviation is possibly due to a residual discrepancy in the simulation of theAPV saturation rather than to a misalignment in the detector. In 2017, the agreement betweendata and simulation is much improved compared to the 2016 data-taking period with the oldAPV settings.

Figure 45 shows the track χ2 probability for the number of degrees of freedom of the trackfits, in both data and simulation. In both years, the abundance of tracks with small track fituncertainties leads to the distribution of the χ2 probability being skewed towards zero. Also


in both years, the simulation underestimates the data for low probability values, and overes-timates the data for high probability values. By comparing the distribution of the RMS of thenormalized residuals (DRNRs) between data and simulation in all the subdetectors for the 2016and 2017 runs, we find that for most of the subdetectors the DRNR in data is shifted towardslarger DRNR values compared with the simulation. This leads to an underestimation of thesimulation, compared with data, for lower values of the χ2 probability.

Overall, good agreement between the data and simulation is seen in both the 2016 data-takingperiod with the old APV settings and in 2017. From the comparison of the data and the simu-lation, we conclude that the updated alignment conditions for the legacy reprocessing in datalead to consistent good agreement with the MC simulation. It also confirms a consistent align-ment of the tracker during Run 2.

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10 SummaryIn this paper, the strategies for and the performance of the alignment of the CMS central trackerduring the data-taking period from 2015 to 2018 have been described.

The alignment was determined from a global track fit, where the module parameters werereleased in addition to the track parameters. Two algorithms based on slightly different ap-proaches were used to perform the minimization of this problem with a large number of param-eters, namely HIPPY and MILLEPEDE-II. Improvements of the software introduced for Run 2were discussed.

Different strategies were applied depending on the number of parameters to determine: atthe beginning of the year and with the very limited amount of data available, the modules ofthe pixel tracker were aligned with respect to the strip tracker; during the year, an automatedalignment procedure was performed for each run, correcting for movements of the mechanicalstructures of the pixel tracker with respect to the strip tracker; finally, in the middle or at the endof the year, once a large amount of data had been recorded (including e.g. a sufficient numberof muon tracks from Z boson decays), all modules of the strip and pixel trackers were alignedin a single fit accounting for time dependence. The last strategy leads to the highest precisionof all and is preferred for the event reconstruction in the context of physics analyses.

Systematic distortions arising from the ageing of the detector, from internal symmetries of theminimization problem or from external constraints, were monitored using specific distribu-tions. Examples of such distributions are the dimuon invariant mass as a function of the kine-matical properties of the outgoing muons and the difference of the mean of the distribution ofthe median of the residuals for modules pointing in opposite directions.

In general, the increased level of radiation compared with data collected before 2012 requiredan updated strategy. As part of the reprocessing of the data recorded during the period from2016 to 2018 after the end of data taking, the alignment parameters were determined with ahigher precision than for the end-of-year reconstruction. This was made possible by artificiallymoving the ladders and panels in the pixel trackers to absorb the gradually accumulating sys-tematic shift induced by the ageing of the modules and by defining finer intervals of validity.

In addition to the alignment of the modules in real data, scenarios for use in simulation werealso derived, aiming to resemble the statistical performance and the systematic effects. A sce-nario was provided for each year separately, without time dependence in the alignment fit.Various comparisons of the performance in data and in simulation have been presented, espe-cially in the context of the reprocessing of the data recorded between 2016 and 2018 after theend of data taking.

AcknowledgmentsWe congratulate our colleagues in the CERN accelerator departments for the excellent perfor-mance of the LHC and thank the technical and administrative staffs at CERN and at other CMSinstitutes for their contributions to the success of the CMS effort. In addition, we gratefully ac-knowledge the computing centres and personnel of the Worldwide LHC Computing Grid andother centres for delivering so effectively the computing infrastructure essential to our analyses.Finally, we acknowledge the enduring support for the construction and operation of the LHC,the CMS detector, and the supporting computing infrastructure provided by the followingfunding agencies: the Austrian Federal Ministry of Education, Science and Research and theAustrian Science Fund; the Belgian Fonds de la Recherche Scientifique, and Fonds voor Weten-

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schappelijk Onderzoek; the Brazilian Funding Agencies (CNPq, CAPES, FAPERJ, FAPERGS,and FAPESP); the Bulgarian Ministry of Education and Science, and the Bulgarian NationalScience Fund; CERN; the Chinese Academy of Sciences, Ministry of Science and Technology,and National Natural Science Foundation of China; the Ministerio de Ciencia Tecnologıa e In-novacion (MINCIENCIAS), Colombia; the Croatian Ministry of Science, Education and Sport,and the Croatian Science Foundation; the Research and Innovation Foundation, Cyprus; theSecretariat for Higher Education, Science, Technology and Innovation, Ecuador; the Ministryof Education and Research, Estonian Research Council via PRG780, PRG803 and PRG445 andEuropean Regional Development Fund, Estonia; the Academy of Finland, Finnish Ministry ofEducation and Culture, and Helsinki Institute of Physics; the Institut National de PhysiqueNucleaire et de Physique des Particules / CNRS, and Commissariat a l’Energie Atomique etaux Energies Alternatives / CEA, France; the Bundesministerium fur Bildung und Forschung,the Deutsche Forschungsgemeinschaft (DFG), under Germany’s Excellence Strategy – EXC2121 “Quantum Universe” – 390833306, and under project number 400140256 - GRK2497, andHelmholtz-Gemeinschaft Deutscher Forschungszentren, Germany; the General Secretariat forResearch and Innovation, Greece; the National Research, Development and Innovation Fund,Hungary; the Department of Atomic Energy and the Department of Science and Technology,India; the Institute for Studies in Theoretical Physics and Mathematics, Iran; the Science Foun-dation, Ireland; the Istituto Nazionale di Fisica Nucleare, Italy; the Ministry of Science, ICT andFuture Planning, and National Research Foundation (NRF), Republic of Korea; the Ministry ofEducation and Science of the Republic of Latvia; the Lithuanian Academy of Sciences; the Min-istry of Education, and University of Malaya (Malaysia); the Ministry of Science of Montenegro;the Mexican Funding Agencies (BUAP, CINVESTAV, CONACYT, LNS, SEP, and UASLP-FAI);the Ministry of Business, Innovation and Employment, New Zealand; the Pakistan AtomicEnergy Commission; the Ministry of Science and Higher Education and the National ScienceCentre, Poland; the Fundacao para a Ciencia e a Tecnologia, grants CERN/FIS-PAR/0025/2019and CERN/FIS-INS/0032/2019, Portugal; JINR, Dubna; the Ministry of Education and Scienceof the Russian Federation, the Federal Agency of Atomic Energy of the Russian Federation,Russian Academy of Sciences, the Russian Foundation for Basic Research, and the NationalResearch Center “Kurchatov Institute”; the Ministry of Education, Science and TechnologicalDevelopment of Serbia; the Secretarıa de Estado de Investigacion, Desarrollo e Innovacion,Programa Consolider-Ingenio 2010, Plan Estatal de Investigacion Cientıfica y Tecnica y de In-novacion 2017–2020, research project IDI-2018-000174 del Principado de Asturias, and FondoEuropeo de Desarrollo Regional, Spain; the Ministry of Science, Technology and Research, SriLanka; the Swiss Funding Agencies (ETH Board, ETH Zurich, PSI, SNF, UniZH, Canton Zurich,and SER); the Ministry of Science and Technology, Taipei; the Thailand Center of Excellencein Physics, the Institute for the Promotion of Teaching Science and Technology of Thailand,Special Task Force for Activating Research and the National Science and Technology Develop-ment Agency of Thailand; the Scientific and Technical Research Council of Turkey, and Turk-ish Atomic Energy Authority; the National Academy of Sciences of Ukraine; the Science andTechnology Facilities Council, UK; the US Department of Energy, and the US National ScienceFoundation.

Individuals have received support from the Marie-Curie programme and the European Re-search Council and Horizon 2020 Grant, contract Nos. 675440, 724704, 752730, 758316,765710, 824093, 884104, and COST Action CA16108 (European Union) the Leventis Foun-dation; the Alfred P. Sloan Foundation; the Alexander von Humboldt Foundation; theBelgian Federal Science Policy Office; the Fonds pour la Formation a la Recherche dansl’Industrie et dans l’Agriculture (FRIA-Belgium); the Agentschap voor Innovatie door Weten-schap en Technologie (IWT-Belgium); the F.R.S.-FNRS and FWO (Belgium) under the “Ex-

62

cellence of Science – EOS” – be.h project n. 30820817; the Beijing Municipal Science &Technology Commission, No. Z191100007219010; the Ministry of Education, Youth andSports (MEYS) of the Czech Republic; the Lendulet (“Momentum”) Programme and theJanos Bolyai Research Scholarship of the Hungarian Academy of Sciences, the New Na-tional Excellence Program UNKP, the NKFIA research grants 123842, 123959, 124845, 124850,125105, 128713, 128786, and 129058 (Hungary); the Council of Scientific and Industrial Re-search, India; the Latvian Council of Science; the National Science Center (Poland), contractsOpus 2014/15/B/ST2/03998 and 2015/19/B/ST2/02861; the Fundacao para a Ciencia e aTecnologia, grant FCT CEECIND/01334/2018; the National Priorities Research Program byQatar National Research Fund; the Ministry of Science and Higher Education, projects no.14.W03.31.0026 and no. FSWW-2020-0008, and the Russian Foundation for Basic Research,project No.19-42-703014 (Russia); the Programa de Excelencia Marıa de Maeztu, and the Pro-grama Severo Ochoa del Principado de Asturias; the Stavros Niarchos Foundation (Greece);the Rachadapisek Sompot Fund for Postdoctoral Fellowship, Chulalongkorn University, andthe Chulalongkorn Academic into Its 2nd Century Project Advancement Project (Thailand);the Kavli Foundation; the Nvidia Corporation; the SuperMicro Corporation; the Welch Foun-dation, contract C-1845; and the Weston Havens Foundation (USA).

The MILLEPEDE-II software is provided by DESY in the framework of the Helmholtz TerascaleAlliance.

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65

A The CMS CollaborationYerevan Physics Institute, Yerevan, ArmeniaA. Tumasyan

Institut fur Hochenergiephysik, Vienna, AustriaW. Adam , J.W. Andrejkovic, T. Bergauer , D. Bloch, S. Chatterjee , M. Dragicevic ,A. Escalante Del Valle , R. Fruhwirth1, V. Hinger , M. Jeitler1 , N. Krammer, L. Lechner ,D. Liko, I. Mikulec, P. Paulitsch, F.M. Pitters, J. Schieck1 , R. Schofbeck , D. Schwarz,H. Steininger, S. Templ , W. Waltenberger , C.-E. Wulz1

Institute for Nuclear Problems, Minsk, BelarusV. Chekhovsky, A. Litomin, V. Makarenko

Universiteit Antwerpen, Antwerpen, BelgiumW. Beaumont, M.R. Darwish2, E.A. De Wolf, T. Janssen , T. Kello3, A. Lelek , H. Rejeb Sfar,P. Van Mechelen , S. Van Putte, N. Van Remortel

Vrije Universiteit Brussel, Brussel, BelgiumF. Blekman , E.S. Bols , J. D’Hondt , M. Delcourt, H. El Faham , S. Lowette ,S. Moortgat , A. Morton , D. Muller , A.R. Sahasransu , S. Tavernier , W. Van Doninck,P. Van Mulders

Universite Libre de Bruxelles, Bruxelles, BelgiumY. Allard, D. Beghin, B. Bilin , B. Clerbaux , G. De Lentdecker, W. Deng, L. Favart ,A. Grebenyuk, D. Hohov, A.K. Kalsi , A. Khalilzadeh, K. Lee, M. Mahdavikhorrami,I. Makarenko , L. Moureaux , L. Petre, A. Popov , N. Postiau, F. Robert, Z. Song,E. Starling , L. Thomas , M. Vanden Bemden, C. Vander Velde , P. Vanlaer ,D. Vannerom , L. Wezenbeek, Y. Yang

Ghent University, Ghent, BelgiumT. Cornelis , D. Dobur, J. Knolle , L. Lambrecht, G. Mestdach, M. Niedziela , C. Roskas,A. Samalan, K. Skovpen , M. Tytgat , B. Vermassen, M. Vit

Universite Catholique de Louvain, Louvain-la-Neuve, BelgiumA. Benecke, A. Bethani , G. Bruno, F. Bury , C. Caputo , P. David , A. Deblaere,C. Delaere , I.S. Donertas , A. Giammanco , K. Jaffel, Sa. Jain , V. Lemaitre, K. Mondal ,J. Prisciandaro, N. Szilasi, A. Taliercio, M. Teklishyn , T.T. Tran, P. Vischia , S. Wertz

Centro Brasileiro de Pesquisas Fisicas, Rio de Janeiro, BrazilG.A. Alves , C. Hensel, A. Moraes

Universidade do Estado do Rio de Janeiro, Rio de Janeiro, BrazilW.L. Alda Junior , M. Alves Gallo Pereira , M. Barroso Ferreira Filho, H. Bran-dao Malbouisson, W. Carvalho , J. Chinellato4, E.M. Da Costa , G.G. Da Silveira5 ,D. De Jesus Damiao , S. Fonseca De Souza , D. Matos Figueiredo, C. Mora Herrera ,K. Mota Amarilo, L. Mundim , H. Nogima, P. Rebello Teles , A. Santoro, S.M. Silva Do Ama-ral , A. Sznajder , M. Thiel, F. Torres Da Silva De Araujo6 , A. Vilela Pereira

Universidade Estadual Paulista (a), Universidade Federal do ABC (b), Sao Paulo, BrazilC.A. Bernardes5 , L. Calligaris , T.R. Fernandez Perez Tomei , E.M. Gregores ,D.S. Lemos , P.G. Mercadante , S.F. Novaes , Sandra S. Padula

Institute for Nuclear Research and Nuclear Energy, Bulgarian Academy of Sciences, Sofia,Bulgaria

https://orcid.org/0000-0001-9099-4341

https://orcid.org/0000-0002-5786-0293

https://orcid.org/0000-0003-2660-0349

https://orcid.org/0000-0003-1967-6783

https://orcid.org/0000-0002-9702-6359

https://orcid.org/0000-0002-2616-4084

https://orcid.org/0000-0002-5141-9560

https://orcid.org/0000-0002-3065-1141

https://orcid.org/0000-0002-1058-8093

https://orcid.org/0000-0002-2332-8784

https://orcid.org/0000-0003-3137-5692

https://orcid.org/0000-0002-6215-7228

https://orcid.org/0000-0001-9226-5812

https://orcid.org/0000-0002-8406-8605

https://orcid.org/0000-0002-3998-4081

https://orcid.org/0000-0001-5862-2775

https://orcid.org/0000-0002-8731-9051

https://orcid.org/0000-0003-4180-8199

https://orcid.org/0000-0002-7366-7098

https://orcid.org/0000-0002-8564-8732

https://orcid.org/0000-0002-9598-6241

https://orcid.org/0000-0001-8894-2390

https://orcid.org/0000-0003-3984-9987

https://orcid.org/0000-0002-6612-3420

https://orcid.org/0000-0002-9919-3492

https://orcid.org/0000-0002-1752-4527

https://orcid.org/0000-0003-1505-1743

https://orcid.org/0000-0002-6792-9522

https://orcid.org/0000-0003-1439-7128

https://orcid.org/0000-0001-8547-8211

https://orcid.org/0000-0003-1645-7454

https://orcid.org/0000-0002-6215-0894

https://orcid.org/0000-0002-8553-4508

https://orcid.org/0000-0002-2310-9266

https://orcid.org/0000-0002-1207-0984

https://orcid.org/0000-0002-4399-7213

https://orcid.org/0000-0002-2756-3853

https://orcid.org/0000-0003-3392-7294

https://orcid.org/0000-0002-7931-4496

https://orcid.org/0000-0002-2747-5095

https://orcid.org/0000-0001-9502-5363

https://orcid.org/0000-0002-4781-5704

https://orcid.org/0000-0001-5745-2567

https://orcid.org/0000-0002-1160-0621

https://orcid.org/0000-0002-3990-2074

https://orcid.org/0000-0002-8150-7043

https://orcid.org/0000-0002-3077-2090

https://orcid.org/0000-0001-7522-4808

https://orcid.org/0000-0001-9260-9371

https://orcid.org/0000-0001-8707-6021

https://orcid.org/0000-0001-7485-412X

https://orcid.org/0000-0001-9640-8294

https://orcid.org/0000-0001-5078-3689

https://orcid.org/0000-0001-5967-1245

https://orcid.org/0000-0002-8506-9714

https://orcid.org/0000-0002-7088-8557

https://orcid.org/0000-0002-8645-3670

https://orcid.org/0000-0002-8369-1446

https://orcid.org/0000-0002-5157-5686

https://orcid.org/0000-0001-5855-9817

https://orcid.org/0000-0003-4296-7028

https://orcid.org/0000-0003-0738-6615

https://orcid.org/0000-0002-5016-6434

https://orcid.org/0000-0003-3514-7056

https://orcid.org/0000-0002-3769-1680

https://orcid.org/0000-0001-7830-0837

https://orcid.org/0000-0003-3915-3170

https://orcid.org/0000-0001-9964-7805

https://orcid.org/0000-0001-9029-8506

https://orcid.org/0000-0002-0209-9687

https://orcid.org/0000-0001-6998-1108

https://orcid.org/0000-0002-4785-3057

https://orcid.org/0000-0003-3177-4626

https://orcid.org/0000-0001-5790-9563

https://orcid.org/0000-0002-9951-9448

https://orcid.org/0000-0002-1809-5226

https://orcid.org/0000-0003-0205-1672

https://orcid.org/0000-0003-1982-8978

https://orcid.org/0000-0001-8333-4302

https://orcid.org/0000-0003-0471-8549

https://orcid.org/0000-0003-3071-0559

66

A. Aleksandrov, G. Antchev , R. Hadjiiska, P. Iaydjiev, M. Misheva, M. Rodozov, M. Shopova,G. Sultanov

University of Sofia, Sofia, BulgariaA. Dimitrov, T. Ivanov, L. Litov , B. Pavlov, P. Petkov, A. Petrov

Beihang University, Beijing, ChinaT. Cheng , T. Javaid7, M. Mittal, H. Wang3, L. Yuan

Department of Physics, Tsinghua University, Beijing, ChinaM. Ahmad , G. Bauer, C. Dozen8 , Z. Hu , J. Martins9 , Y. Wang, K. Yi10,11

Institute of High Energy Physics, Beijing, ChinaE. Chapon , G.M. Chen7 , H.S. Chen7 , M. Chen , F. Iemmi, A. Kapoor , D. Leggat,H. Liao, Z.-A. Liu7 , V. Milosevic , F. Monti , R. Sharma , J. Tao , J. Thomas-Wilsker,J. Wang , H. Zhang , J. Zhao

State Key Laboratory of Nuclear Physics and Technology, Peking University, Beijing, ChinaA. Agapitos, Y. An, Y. Ban, C. Chen, A. Levin , Q. Li , X. Lyu, Y. Mao, S.J. Qian, D. Wang ,Q. Wang12 , J. Xiao

Sun Yat-Sen University, Guangzhou, ChinaM. Lu, Z. You

Institute of Modern Physics and Key Laboratory of Nuclear Physics and Ion-beam Applica-tion (MOE) - Fudan University, Shanghai, ChinaX. Gao3, H. Okawa

Zhejiang University, Hangzhou, China, Zhejiang, ChinaZ. Lin , M. Xiao

Universidad de Los Andes, Bogota, ColombiaC. Avila , A. Cabrera , C. Florez , J. Fraga

Universidad de Antioquia, Medellin, ColombiaJ. Mejia Guisao, F. Ramirez, J.D. Ruiz Alvarez , C.A. Salazar Gonzalez

University of Split, Faculty of Electrical Engineering, Mechanical Engineering and Naval Ar-chitecture, Split, CroatiaD. Giljanovic, N. Godinovic , D. Lelas , I. Puljak

University of Split, Faculty of Science, Split, CroatiaZ. Antunovic, M. Kovac, T. Sculac

Institute Rudjer Boskovic, Zagreb, CroatiaV. Brigljevic , D. Ferencek , D. Majumder , S. Mishra, M. Roguljic, A. Starodumov13 ,T. Susa

University of Cyprus, Nicosia, CyprusA. Attikis , K. Christoforou, E. Erodotou, A. Ioannou, G. Kole , M. Kolosova,S. Konstantinou, J. Mousa , C. Nicolaou, F. Ptochos , P.A. Razis, H. Rykaczewski, H. Saka

Charles University, Prague, Czech RepublicM. Finger14, M. Finger Jr.14 , A. Kveton

Escuela Politecnica Nacional, Quito, EcuadorE. Ayala

https://orcid.org/0000-0003-3210-5037

https://orcid.org/0000-0002-8511-6883

https://orcid.org/0000-0003-2954-9315

https://orcid.org/0000-0001-9933-995X

https://orcid.org/0000-0002-4301-634X

https://orcid.org/0000-0001-8209-4343

https://orcid.org/0000-0002-2120-2782

https://orcid.org/0000-0001-6968-9828

https://orcid.org/0000-0002-2629-5420

https://orcid.org/0000-0001-8672-8227

https://orcid.org/0000-0003-0489-9669

https://orcid.org/0000-0002-1844-1504

https://orcid.org/0000-0002-2896-1386

https://orcid.org/0000-0002-1173-0696

https://orcid.org/0000-0001-5846-3655

https://orcid.org/0000-0003-1181-1426

https://orcid.org/0000-0003-2006-3490

https://orcid.org/0000-0002-4963-0877

https://orcid.org/0000-0001-8843-5209

https://orcid.org/0000-0001-8365-7726

https://orcid.org/0000-0001-9565-4186

https://orcid.org/0000-0002-8290-0517

https://orcid.org/0000-0002-9013-1199

https://orcid.org/0000-0003-1014-8677

https://orcid.org/0000-0001-8324-3291

https://orcid.org/0000-0002-2548-6567

https://orcid.org/0000-0003-1812-3474

https://orcid.org/0000-0001-9628-9336

https://orcid.org/0000-0002-5610-2693

https://orcid.org/0000-0002-0486-6296

https://orcid.org/0000-0002-3222-0249

https://orcid.org/0000-0002-3306-0363

https://orcid.org/0000-0002-0394-4870

https://orcid.org/0000-0002-4674-9450

https://orcid.org/0000-0002-8269-5760

https://orcid.org/0000-0001-7387-3812

https://orcid.org/0000-0002-9578-4105

https://orcid.org/0000-0001-5847-0062

https://orcid.org/0000-0001-9116-1202

https://orcid.org/0000-0002-7578-0027

https://orcid.org/0000-0001-9570-9255

https://orcid.org/0000-0001-7430-2552

https://orcid.org/0000-0002-4443-3794

https://orcid.org/0000-0002-3285-1497

https://orcid.org/0000-0002-2978-2718

https://orcid.org/0000-0002-3432-3452

https://orcid.org/0000-0001-7616-2573

https://orcid.org/0000-0003-3155-2484

67

Universidad San Francisco de Quito, Quito, EcuadorE. Carrera Jarrin

Academy of Scientific Research and Technology of the Arab Republic of Egypt, Egyp-tian Network of High Energy Physics, Cairo, EgyptH. Abdalla15 , Y. Assran16,17

Center for High Energy Physics (CHEP-FU), Fayoum University, El-Fayoum, EgyptM.A. Mahmoud , Y. Mohammed

National Institute of Chemical Physics and Biophysics, Tallinn, EstoniaI. Ahmed18, S. Bhowmik , R.K. Dewanjee , K. Ehataht, M. Kadastik, S. Nandan, C. Nielsen,J. Pata, M. Raidal , L. Tani, C. Veelken

Department of Physics, University of Helsinki, Helsinki, FinlandP. Eerola , L. Forthomme , H. Kirschenmann , K. Osterberg , M. Voutilainen

Helsinki Institute of Physics, Helsinki, FinlandS. Bharthuar, E. Brucken , F. Garcia , J. Havukainen , M.S. Kim , R. Kinnunen,T. Lampen, K. Lassila-Perini , S. Lehti , T. Linden, M. Lotti, L. Martikainen, M. Myllymaki,J. Ott , H. Siikonen, E. Tuominen , J. Tuominiemi

Lappeenranta University of Technology, Lappeenranta, FinlandP. Luukka , H. Petrow, T. Tuuva

IRFU, CEA, Universite Paris-Saclay, Gif-sur-Yvette, FranceC. Amendola , M. Besancon, F. Couderc , M. Dejardin, D. Denegri, J.L. Faure, F. Ferri ,S. Ganjour, A. Givernaud, P. Gras, G. Hamel de Monchenault , P. Jarry, B. Lenzi , E. Locci,J. Malcles, J. Rander, A. Rosowsky , M.O. Sahin , A. Savoy-Navarro19, M. Titov , G.B. Yu

Laboratoire Leprince-Ringuet, CNRS/IN2P3, Ecole Polytechnique, Institut Polytech-nique de Paris, Palaiseau, FranceS. Ahuja , F. Beaudette , M. Bonanomi , A. Buchot Perraguin, P. Busson, A. Cappati,C. Charlot, O. Davignon, B. Diab, G. Falmagne , S. Ghosh, R. Granier de Cassagnac ,A. Hakimi, I. Kucher , J. Motta, M. Nguyen , C. Ochando , P. Paganini , J. Rembser,R. Salerno , U. Sarkar , J.B. Sauvan , Y. Sirois , A. Tarabini, A. Zabi, A. Zghiche

Universite de Strasbourg, CNRS, IPHC UMR 7178, Strasbourg, FranceJ.-L. Agram20 , J. Andrea, D. Apparu, D. Bloch , C. Bonnin, G. Bourgatte, J.-M. Brom,E.C. Chabert, L. Charles, C. Collard , E. Dangelser, D. Darej, J.-C. Fontaine20, U. Goerlach,C. Grimault, L. Gross, C. Haas, M. Krauth, A.-C. Le Bihan, E. Nibigira , N. Ollivier-henry,E. Silva Jimenez, P. Van Hove

Institut de Physique des 2 Infinis de Lyon (IP2I ), Villeurbanne, FranceE. Asilar , G. Baulieu, S. Beauceron , C. Bernet , G. Boudoul, C. Camen, L. Caponetto,A. Carle, N. Chanon , D. Contardo, P. Dene, P. Depasse , T. Dupasquier, H. El Mamouni,J. Fay, G. Galbit, S. Gascon , M. Gouzevitch , B. Ille, I.B. Laktineh, H. Lattaud ,A. Lesauvage , M. Lethuillier , N. Lumb, L. Mirabito, B. Nodari, S. Perries, K. Shchablo,V. Sordini , L. Torterotot , G. Touquet, M. Vander Donckt, S. Viret

Georgian Technical University, Tbilisi, GeorgiaI. Lomidze, T. Toriashvili21, Z. Tsamalaidze14

RWTH Aachen University, I. Physikalisches Institut, Aachen, GermanyC. Autermann , V. Botta, L. Feld , W. Karpinski, M.K. Kiesel, K. Klein, M. Lipinski,

https://orcid.org/0000-0002-0857-8507

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https://orcid.org/0000-0003-1260-973X

https://orcid.org/0000-0001-6645-6244

https://orcid.org/0000-0001-7040-9491

https://orcid.org/0000-0002-3244-0591

https://orcid.org/0000-0002-3302-336X

https://orcid.org/0000-0001-7369-2536

https://orcid.org/0000-0003-4807-0414

https://orcid.org/0000-0002-5200-6477

https://orcid.org/0000-0001-6066-8756

https://orcid.org/0000-0002-4023-7964

https://orcid.org/0000-0003-2898-6900

https://orcid.org/0000-0003-0392-8691

https://orcid.org/0000-0002-5502-1795

https://orcid.org/0000-0003-1370-5598

https://orcid.org/0000-0001-9337-5722

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D. Louis, D. Meuser, A. Pauls, G. Pierschel, M.P. Rauch, N. Rowert, C. Schomakers, J. Schulz,M. Teroerde , M. Wlochal

RWTH Aachen University, III. Physikalisches Institut A, Aachen, GermanyA. Dodonova, D. Eliseev, M. Erdmann , P. Fackeldey , B. Fischer, S. Ghosh ,T. Hebbeker , K. Hoepfner, F. Ivone, L. Mastrolorenzo, M. Merschmeyer , A. Meyer ,G. Mocellin, S. Mondal, S. Mukherjee , D. Noll , A. Novak, T. Pook , A. Pozdnyakov ,Y. Rath, H. Reithler, J. Roemer, A. Schmidt , S.C. Schuler, A. Sharma , L. Vigilante,S. Wiedenbeck, S. Zaleski

RWTH Aachen University, III. Physikalisches Institut B, Aachen, GermanyC. Dziwok, G. Flugge, W. Haj Ahmad22 , O. Hlushchenko, T. Kress, A. Nowack , C. Pistone,O. Pooth, D. Roy , H. Sert , A. Stahl18 , T. Ziemons , A. Zotz

Deutsches Elektronen-Synchrotron, Hamburg, GermanyH. Aarup Petersen, M. Aldaya Martin, P. Asmuss, S. Baxter, M. Bayatmakou, O. Behnke,A. Bermudez Martınez, D. Bertsche , S. Bhattacharya, A.A. Bin Anuar , K. Borras23,D. Brunner, A. Campbell , A. Cardini , C. Cheng, F. Colombina, S. Consuegra Rodrıguez ,G. Correia Silva, V. Danilov, M. De Silva, L. Didukh, D. Domınguez Damiani, G. Eckerlin,D. Eckstein, L.I. Estevez Banos , O. Filatov , E. Gallo24, A. Geiser, A. Giraldi,J.M. Grados Luyando, A. Grohsjean , M. Guthoff, A. Jafari25 , N.Z. Jomhari , H. Jung ,A. Kasem23 , M. Kasemann , H. Kaveh , C. Kleinwort , D. Krucker , W. Lange,J. Lidrych , K. Lipka, W. Lohmann26, R. Mankel, H. Maser, I.-A. Melzer-Pellmann ,M. Mendizabal Morentin, J. Metwally, A.B. Meyer , M. Meyer , G. Mittag, J. Mnich ,C. Muhl, A. Mussgiller, Y. Otarid, D. Perez Adan , D. Pitzl, A. Raspereza, O. Reichelt,B. Ribeiro Lopes, J. Rubenach, A. Saggio , A. Saibel , M. Savitskyi , M. Scham27,V. Scheurer, P. Schutze, C. Schwanenberger24 , M. Shchedrolosiev, R. Shevchenko ,R.E. Sosa Ricardo , D. Stafford, R. Stever, N. Tonon , M. Van De Klundert , A. Velyka,R. Walsh , D. Walter, Y. Wen , K. Wichmann, L. Wiens, C. Wissing, S. Wuchterl , A. Zuber

University of Hamburg, Hamburg, GermanyR. Aggleton, S. Albrecht , S. Bein , L. Benato , H. Biskop, P. Buhmann, P. Connor ,K. De Leo , M. Eich, F. Feindt, A. Frohlich, C. Garbers , E. Garutti , P. Gunnellini,M. Hajheidari, J. Haller , A. Hinzmann , H.R. Jabusch, G. Kasieczka, R. Klanner ,R. Kogler , T. Kramer, V. Kutzner, J. Lange , T. Lange , A. Lobanov , A. Malara ,S. Martens, M. Mrowietz, C.E.N. Niemeyer, A. Nigamova, Y. Nissan, K.J. Pena Rodriguez,O. Rieger, P. Schleper, M. Schroder , J. Schwandt , J. Sonneveld , H. Stadie, G. Steinbruck,A. Tews, B. Vormwald , J. Wellhausen, I. Zoi

Karlsruher Institut fuer Technologie, Karlsruhe, GermanyL.E. Ardila-Perez, M. Balzer, T. Barvich, J. Bechtel , T. Blank, S. Brommer, M. Burkart,E. Butz , M. Caselle, R. Caspart , T. Chwalek, W. De Boer†, A. Dierlamm, A. Droll,K. El Morabit, N. Faltermann , M. Giffels, J.o. Gosewisch, A. Gottmann, F. Hartmann18 ,C. Heidecker, U. Husemann , P. Keicher, R. Koppenhofer, S. Maier, M. Metzler, S. Mitra ,Th. Muller, M. Neufeld, M. Neukum, A. Nurnberg, G. Quast , K. Rabbertz , J. Rauser,O. Sander, D. Savoiu , D. Schell , M. Schnepf, D. Seith, I. Shvetsov, H.J. Simonis, J. Stanulla,P. Steck, R. Ulrich , J. Van Der Linden, R.F. Von Cube, M. Wassmer, M. Weber , A. Weddigen,S. Wieland, F. Wittig, R. Wolf , S. Wozniewski, S. Wunsch

Institute of Nuclear and Particle Physics (INPP), NCSR Demokritos, Aghia Paraskevi,GreeceG. Anagnostou, P. Assiouras , G. Daskalakis, T. Geralis , I. Kazas, A. Kyriakis, D. Loukas,

https://orcid.org/0000-0002-5892-1377

https://orcid.org/0000-0002-1653-1303

https://orcid.org/0000-0003-4932-7162

https://orcid.org/0000-0001-6717-0803

https://orcid.org/0000-0002-9736-266X

https://orcid.org/0000-0003-2081-7141

https://orcid.org/0000-0001-9598-6623

https://orcid.org/0000-0001-6341-9982

https://orcid.org/0000-0002-0176-2360

https://orcid.org/0000-0002-9635-5126

https://orcid.org/0000-0003-3478-9081

https://orcid.org/0000-0003-2711-8984

https://orcid.org/0000-0002-5295-1460

https://orcid.org/0000-0003-1491-0446

https://orcid.org/0000-0002-3522-5926

https://orcid.org/0000-0002-8659-7762

https://orcid.org/0000-0003-0716-6727

https://orcid.org/0000-0002-8369-7506

https://orcid.org/0000-0003-1697-2130

https://orcid.org/0000-0003-2074-4691

https://orcid.org/0000-0002-2988-9830

https://orcid.org/0000-0003-4439-5748

https://orcid.org/0000-0003-1803-0999

https://orcid.org/0000-0002-1383-1837

https://orcid.org/0000-0001-6195-3102

https://orcid.org/0000-0001-9850-6170

https://orcid.org/0000-0003-0748-8494

https://orcid.org/0000-0001-7327-1870

https://orcid.org/0000-0001-9127-7408

https://orcid.org/0000-0002-2964-9845

https://orcid.org/0000-0002-6753-7254

https://orcid.org/0000-0002-0429-2448

https://orcid.org/0000-0002-3273-5859

https://orcid.org/0000-0002-9017-9504

https://orcid.org/0000-0003-1610-8844

https://orcid.org/0000-0003-1439-0196

https://orcid.org/0000-0001-7707-919X

https://orcid.org/0000-0001-8532-2356

https://orcid.org/0000-0003-2436-8195

https://orcid.org/0000-0001-7242-8426

https://orcid.org/0000-0003-3416-0726

https://orcid.org/0000-0002-7385-3317

https://orcid.org/0000-0002-9932-7622

https://orcid.org/0000-0002-9952-9267

https://orcid.org/0000-0001-6699-6662

https://orcid.org/0000-0002-3236-4090

https://orcid.org/0000-0002-2240-6699

https://orcid.org/0000-0003-4301-2688

https://orcid.org/0000-0001-8596-2812

https://orcid.org/0000-0002-3872-4114

https://orcid.org/0000-0002-8724-9604

https://orcid.org/0000-0001-9955-9258

https://orcid.org/0000-0002-5960-6803

https://orcid.org/0000-0001-9387-7407

https://orcid.org/0000-0001-5135-7489

https://orcid.org/0000-0003-2500-1061

https://orcid.org/0000-0002-8908-409X

https://orcid.org/0000-0001-5094-2256

https://orcid.org/0000-0003-0634-5539

https://orcid.org/0000-0001-9347-7657

https://orcid.org/0000-0002-2633-4696

https://orcid.org/0000-0002-7004-9227

https://orcid.org/0000-0002-5336-4399

https://orcid.org/0000-0001-7513-6330

https://orcid.org/0000-0001-6242-7331

https://orcid.org/0000-0002-5376-0877

https://orcid.org/0000-0001-8645-9282

https://orcid.org/0000-0001-8058-9828

https://orcid.org/0000-0002-0052-597X

https://orcid.org/0000-0001-8362-4414

https://orcid.org/0000-0003-2607-7287

https://orcid.org/0000-0002-5738-9446

https://orcid.org/0000-0001-5245-7318

https://orcid.org/0000-0002-2403-5801

https://orcid.org/0000-0002-5502-9412

https://orcid.org/0000-0001-6506-3107

https://orcid.org/0000-0001-8989-8387

https://orcid.org/0000-0002-6198-8388

https://orcid.org/0000-0002-3060-2278

https://orcid.org/0000-0002-4021-4260

https://orcid.org/0000-0001-7040-9846

https://orcid.org/0000-0001-6794-7475

https://orcid.org/0000-0001-6954-3490

https://orcid.org/0000-0002-2535-402X

https://orcid.org/0000-0002-3639-2267

https://orcid.org/0000-0001-9456-383X

https://orcid.org/0000-0002-5152-9006

https://orcid.org/0000-0001-7188-979X

69

A. Papadopoulos, A. Stakia

National and Kapodistrian University of Athens, Athens, GreeceM. Diamantopoulou, D. Karasavvas, G. Karathanasis, P. Kontaxakis , C.K. Koraka,A. Manousakis-Katsikakis, A. Panagiotou, I. Papavergou, N. Saoulidou , K. Theofilatos ,E. Tziaferi , K. Vellidis, E. Vourliotis

National Technical University of Athens, Athens, GreeceG. Bakas, K. Kousouris , I. Papakrivopoulos, G. Tsipolitis, A. Zacharopoulou, A. Zografos

University of Ioannina, Ioannina, GreeceK. Adamidis, I. Bestintzanos, I. Evangelou , C. Foudas, P. Gianneios, P. Katsoulis, P. Kokkas,N. Manthos, I. Papadopoulos , J. Strologas

MTA-ELTE Lendulet CMS Particle and Nuclear Physics Group, Eotvos Lorand University,Budapest, HungaryM. Csanad , K. Farkas, M.M.A. Gadallah28 , S. Lokos29 , P. Major, K. Mandal ,A. Mehta , G. Pasztor , A.J. Radl, O. Suranyi, G.I. Veres

Wigner Research Centre for Physics, Budapest, HungaryT. Balazs , M. Bartok30 , G. Bencze, C. Hajdu , D. Horvath31 , K. Marton, F. Sikler ,V. Veszpremi

Institute of Nuclear Research ATOMKI, Debrecen, HungaryS. Czellar, J. Karancsi30 , J. Molnar, Z. Szillasi, D. Teyssier

Institute of Physics, University of Debrecen, Debrecen, HungaryP. Raics, Z.L. Trocsanyi32 , B. Ujvari

Karoly Robert Campus, MATE Institute of Technology, Gyongyos, HungaryT. Csorgo33 , F. Nemes33, T. Novak

Indian Institute of Science (IISc), Bangalore, IndiaS. Choudhury, J.R. Komaragiri , D. Kumar, L. Panwar , P.C. Tiwari

National Institute of Science Education and Research, HBNI, Bhubaneswar, IndiaS. Bahinipati34 , A.K. Das, C. Kar , P. Mal, T. Mishra , R. Mohanty, V.K. Muraleedha-ran Nair Bindhu35, A. Nayak35 , P. Saha, N. Sur , S.K. Swain, D. Vats35

Panjab University, Chandigarh, IndiaS. Bansal , S.B. Beri, V. Bhatnagar , G. Chaudhary , S. Chauhan , N. Dhingra36 ,R. Gupta, A. Kaur, M. Kaur , S. Kaur, P. Kumari , M. Meena, K. Sandeep , J.B. Singh ,A.K. Virdi

University of Delhi, Delhi, IndiaA. Ahmed, A. Bhardwaj , B.C. Choudhary , M. Gola, C. Jain, G. Jain, S. Keshri ,A. Kumar , M. Naimuddin , P. Priyanka , K. Ranjan, S. Saumya, A. Shah

Saha Institute of Nuclear Physics, HBNI, Kolkata, IndiaM. Bharti37, R. Bhattacharya, S. Bhattacharya , D. Bhowmik, S. Dutta, S. Dutta,B. Gomber38 , M. Maity39, P. Palit , P.K. Rout , G. Saha, B. Sahu , S. Sarkar, M. Sharan,B. Singh37, S. Thakur37

Indian Institute of Technology Madras, Madras, IndiaP.K. Behera , S.C. Behera, P. Kalbhor , A. Muhammad, R. Pradhan, P.R. Pujahari,A. Sharma , A.K. Sikdar

https://orcid.org/0000-0001-6277-7171

https://orcid.org/0000-0002-4860-5979

https://orcid.org/0000-0001-6958-4196

https://orcid.org/0000-0001-8448-883X

https://orcid.org/0000-0003-4958-0408

https://orcid.org/0000-0002-6360-0869

https://orcid.org/0000-0002-5903-5481

https://orcid.org/0000-0002-9937-3063

https://orcid.org/0000-0002-2225-7160

https://orcid.org/0000-0002-3154-6925

https://orcid.org/0000-0002-8305-6661

https://orcid.org/0000-0002-4447-4836

https://orcid.org/0000-0002-3966-7182

https://orcid.org/0000-0002-0433-4484

https://orcid.org/0000-0003-0707-9762

https://orcid.org/0000-0002-5440-4356

https://orcid.org/0000-0002-7516-1752

https://orcid.org/0000-0002-4440-2701

https://orcid.org/0000-0002-7193-800X

https://orcid.org/0000-0003-0091-477X

https://orcid.org/0000-0001-9608-3901

https://orcid.org/0000-0001-9783-0315

https://orcid.org/0000-0003-0802-7665

https://orcid.org/0000-0002-2129-1279

https://orcid.org/0000-0002-9110-9663

https://orcid.org/0000-0002-9344-6655

https://orcid.org/0000-0003-2461-4907

https://orcid.org/0000-0002-3667-3843

https://orcid.org/0000-0002-3744-5332

https://orcid.org/0000-0002-6407-6974

https://orcid.org/0000-0002-2121-3932

https://orcid.org/0000-0002-7716-4981

https://orcid.org/0000-0001-5233-553X

https://orcid.org/0000-0003-1992-0336

https://orcid.org/0000-0002-8392-9610

https://orcid.org/0000-0003-0168-3336

https://orcid.org/0000-0001-6974-4129

https://orcid.org/0000-0002-7200-6204

https://orcid.org/0000-0002-3440-2767

https://orcid.org/0000-0002-6623-8586

https://orcid.org/0000-0002-3220-3668

https://orcid.org/0000-0001-9029-2462

https://orcid.org/0000-0002-0866-8932

https://orcid.org/0000-0002-7544-3258

https://orcid.org/0000-0001-5029-1887

https://orcid.org/0000-0003-3280-2350

https://orcid.org/0000-0003-3407-4094

https://orcid.org/0000-0003-4542-386X

https://orcid.org/0000-0002-0933-685X

https://orcid.org/0000-0002-6157-2016

https://orcid.org/0000-0002-8110-4957

https://orcid.org/0000-0002-4446-0258

https://orcid.org/0000-0002-1948-029X

https://orcid.org/0000-0001-8149-6180

https://orcid.org/0000-0002-8073-5140

https://orcid.org/0000-0002-1527-2266

https://orcid.org/0000-0002-5892-3743

https://orcid.org/0000-0002-0688-923X

70

Bhabha Atomic Research Centre, Mumbai, IndiaD. Dutta , V. Jha, V. Kumar , D.K. Mishra, K. Naskar40, P.K. Netrakanti, L.M. Pant,P. Shukla

Tata Institute of Fundamental Research-A, Mumbai, IndiaT. Aziz, S. Dugad, M. Kumar

Tata Institute of Fundamental Research-B, Mumbai, IndiaS. Banerjee , R. Chudasama, M. Guchait, S. Karmakar, S. Kumar, G. Majumder, K. Mazumdar,S. Mukherjee

Indian Institute of Science Education and Research (IISER), Pune, IndiaK. Alpana, S. Dube , B. Kansal, A. Laha, S. Pandey , A. Rane , A. Rastogi , S. Sharma

Isfahan University of Technology, Isfahan, IranH. Bakhshiansohi12 , E. Khazaie, M. Zeinali41

Institute for Research in Fundamental Sciences (IPM), Tehran, IranS.M. Abbas, S. Chenarani42, S.M. Etesami , M. Khakzad , M. Mohammadi Najafabadi

University College Dublin, Dublin, IrelandM. Grunewald

INFN Sezione di Bari a, Bari, Italy, Universita di Bari b, Bari, Italy, Politecnico di Bari c, Bari,ItalyM. Abbresciaa,b , R. Alya ,b ,43 , C. Arutaa ,b, P. Cariolaa, A. Colaleoa , D. Creanzaa,c ,N. De Filippisa,c , M. De Palmaa,b , G. De Robertisa , A. Di Florioa,b, A. Di Pilatoa,b ,W. Elmetenaweea,b , L. Fiorea , A. Gelmia,b , M. Gula , G. Iasellia,c , M. Incea,b ,S. Lezkia,b , F. Loddoa , G. Maggia,c , M. Maggia , I. Margjekaa,b, S. Martiradonnaa,b,V. Mastrapasquaa,b , J.A. Merlina, A. Mongellia,b, S. Mya,b , S. Nuzzoa,b , A. Pellecchiaa,b,A. Pompilia,b , G. Pugliesea,c , D. Ramos, A. Ranieria , G. Selvaggia,b , L. Silvestrisa ,F.M. Simonea,b , R. Vendittia , P. Verwilligena

INFN Sezione di Bologna a, Bologna, Italy, Universita di Bologna b, Bologna, ItalyG. Abbiendia , C. Battilanaa,b , D. Bonacorsia,b , L. Borgonovia, L. Brigliadoria,R. Campaninia ,b , P. Capiluppia,b , A. Castroa,b , F.R. Cavalloa , M. Cuffiania,b ,G.M. Dallavallea , T. Diotalevia,b , F. Fabbria , A. Fanfania,b , P. Giacomellia ,L. Giommia,b , C. Grandia , L. Guiduccia,b, S. Lo Meoa,44, L. Lunertia,b, S. Marcellinia ,G. Masettia , F.L. Navarriaa,b , A. Perrottaa , F. Primaveraa,b , A.M. Rossia,b ,T. Rovellia,b , G.P. Sirolia,b

INFN Sezione di Catania a, Catania, Italy, Universita di Catania b, Catania, ItalyS. Albergoa,b ,45 , S. Costaa,b ,45 , A. Di Mattiaa , R. Potenzaa,b, M.A. Saizua,46,A. Tricomia,b ,45 , C. Tuvea,b

INFN Sezione di Firenze a, Firenze, Italy, Universita di Firenze b, Firenze, ItalyG. Barbaglia , M. Brianzia, A. Cassesea , R. Ceccarellia,b, R. Ciaranfia, V. Ciullia,b ,C. Civininia , R. D’Alessandroa,b , F. Fioria,b, E. Focardia,b , G. Latinoa,b , P. Lenzia,b ,M. Lizzoa,b, M. Meschinia , S. Paolettia , R. Seiditaa,b, G. Sguazzonia , L. Viliania

INFN Laboratori Nazionali di Frascati, Frascati, ItalyL. Benussi , S. Bianco , D. Piccolo

INFN Sezione di Genova a, Genova, Italy, Universita di Genova b, Genova, ItalyM. Bozzoa,b , F. Ferroa , R. Mulargiaa ,b, E. Robuttia , S. Tosia,b

https://orcid.org/0000-0002-0046-9568

https://orcid.org/0000-0001-8694-8326

https://orcid.org/0000-0001-8118-5331

https://orcid.org/0000-0002-7953-4683

https://orcid.org/0000-0003-3122-0594

https://orcid.org/0000-0002-5145-3777

https://orcid.org/0000-0003-0440-6019

https://orcid.org/0000-0001-8444-2807

https://orcid.org/0000-0003-1245-6710

https://orcid.org/0000-0001-6886-0726

https://orcid.org/0000-0001-5741-3357

https://orcid.org/0000-0001-6501-4137

https://orcid.org/0000-0002-2212-5715

https://orcid.org/0000-0001-6131-5987

https://orcid.org/0000-0002-5754-0388

https://orcid.org/0000-0001-8727-7544

https://orcid.org/0000-0001-6808-1335

https://orcid.org/0000-0002-0711-6319

https://orcid.org/0000-0001-6153-3044

https://orcid.org/0000-0002-0625-6811

https://orcid.org/0000-0001-8240-1913

https://orcid.org/0000-0001-8261-6236

https://orcid.org/0000-0002-9233-3632

https://orcid.org/0000-0001-7069-0252

https://orcid.org/0000-0002-9470-1320

https://orcid.org/0000-0002-9211-2709

https://orcid.org/0000-0002-5704-1896

https://orcid.org/0000-0003-2546-5341

https://orcid.org/0000-0001-6907-0195

https://orcid.org/0000-0002-6909-774X

https://orcid.org/0000-0001-9517-6815

https://orcid.org/0000-0001-5391-7689

https://orcid.org/0000-0002-8431-3922

https://orcid.org/0000-0002-9082-5924

https://orcid.org/0000-0002-9938-2680

https://orcid.org/0000-0003-1089-6317

https://orcid.org/0000-0003-1291-4005

https://orcid.org/0000-0001-5460-2638

https://orcid.org/0000-0001-7912-4062

https://orcid.org/0000-0003-0093-6741

https://orcid.org/0000-0002-8985-4891

https://orcid.org/0000-0002-1924-983X

https://orcid.org/0000-0001-6925-8649

https://orcid.org/0000-0002-9285-8631

https://orcid.org/0000-0003-4499-7562

https://orcid.org/0000-0002-3753-3068

https://orcid.org/0000-0002-0835-9574

https://orcid.org/0000-0002-2744-0597

https://orcid.org/0000-0003-4485-1897

https://orcid.org/0000-0003-2527-0456

https://orcid.org/0000-0002-0326-7515

https://orcid.org/0000-0003-2510-5039

https://orcid.org/0000-0002-8614-0420

https://orcid.org/0000-0003-0780-8785

https://orcid.org/0000-0002-8446-9660

https://orcid.org/0000-0003-2256-4117

https://orcid.org/0000-0002-6368-7220

https://orcid.org/0000-0003-3539-4313

https://orcid.org/0000-0001-5998-3070

https://orcid.org/0000-0002-1233-8100

https://orcid.org/0000-0002-6377-800X

https://orcid.org/0000-0001-7961-4889

https://orcid.org/0000-0002-7996-7139

https://orcid.org/0000-0001-6253-8656

https://orcid.org/0000-0002-5973-1305

https://orcid.org/0000-0002-9746-4842

https://orcid.org/0000-0002-3528-4125

https://orcid.org/0000-0001-7901-4189

https://orcid.org/0000-0001-9919-0569

https://orcid.org/0000-0002-9964-015X

https://orcid.org/0000-0002-5071-5501

https://orcid.org/0000-0003-0739-3153

https://orcid.org/0000-0002-1738-8676

https://orcid.org/0000-0003-3010-4516

https://orcid.org/0000-0003-1947-3396

https://orcid.org/0000-0002-4952-3799

https://orcid.org/0000-0001-7997-0306

https://orcid.org/0000-0002-3763-5267

https://orcid.org/0000-0002-4098-3502

https://orcid.org/0000-0002-6927-8807

https://orcid.org/0000-0002-9161-3990

https://orcid.org/0000-0003-3592-9509

https://orcid.org/0000-0002-0791-3350

https://orcid.org/0000-0002-1909-6343

https://orcid.org/0000-0002-2363-8889

https://orcid.org/0000-0002-8300-4124

https://orcid.org/0000-0001-5404-543X

https://orcid.org/0000-0002-1715-0457

https://orcid.org/0000-0002-7663-0805

https://orcid.org/0000-0001-9038-4500

https://orcid.org/0000-0002-7275-9193

71

INFN Sezione di Milano-Bicocca a, Milano, Italy, Universita di Milano-Bicocca b, Milano,ItalyA. Benagliaa , G. Boldrini , F. Brivioa,b, F. Cetorellia ,b, F. De Guioa,b , M.E. Dinardoa,b ,P. Dinia , S. Gennaia , A. Ghezzia ,b , P. Govonia,b , L. Guzzia,b , M.T. Lucchinia,b ,M. Malbertia, S. Malvezzia , A. Massironia , D. Menascea , L. Moronia ,M. Paganonia,b , D. Pedrinia , B.S. Pinolini, S. Ragazzia,b , N. Redaellia ,T. Tabarelli de Fatisa,b , D. Valsecchia,b ,18, D. Zuoloa,b

INFN Sezione di Napoli a, Napoli, Italy, Universita di Napoli ’Federico II’ b, Napoli, Italy,Universita della Basilicata c, Potenza, Italy, Universita G. Marconi d, Roma, ItalyS. Buontempoa , F. Carnevalia,b, N. Cavalloa,c , A. De Iorioa,b , F. Fabozzia,c ,A.O.M. Iorioa,b , L. Listaa,b , S. Meolaa,d,18 , P. Paoluccia,18 , B. Rossia , C. Sciaccaa,b

INFN Sezione di Padova a, Padova, Italy, Universita di Padova b, Padova, Italy, Universitadi Trento c, Trento, ItalyP. Azzia , N. Bacchettaa , D. Biselloa,b , P. Bortignona , A. Bragagnoloa,b ,R. Carlina,b , P. Checchiaa , T. Dorigoa , U. Dossellia , F. Gasparinia,b ,U. Gasparinia ,b , G. Grosso, S.Y. Hoha,b , L. Layera,47, E. Lusiani , M. Margonia,b ,A.T. Meneguzzoa,b , J. Pazzinia,b , P. Ronchesea,b , R. Rossina,b, F. Simonettoa,b ,G. Stronga , M. Tosia,b , H. Yarara,b, M. Zanettia,b , P. Zottoa,b , A. Zucchettaa,b ,G. Zumerlea ,b

INFN Sezione di Pavia a, Pavia, Italy, Universita di Pavia b, Pavia, ItalyC. Aime‘a ,b, A. Braghieria , S. Calzaferria,b, D. Fiorinaa ,b , L. Gaionia , M. Manghisonia ,P. Montagnaa,b, L. Rattia , S.P. Rattia,b, V. Rea , C. Riccardia,b , E. Riceputia, P. Salvinia ,G. Traversia , I. Vaia , P. Vituloa,b

INFN Sezione di Perugia a, Perugia, Italy, Universita di Perugia b, Perugia, ItalyP. Asenova,48 , G. Baldinelli, F. Bianchi, G.M. Bileia , S. Bizzagliaa, B. Checcuccia ,D. Ciangottinia,b , L. Fanoa,b , L. Farnesinia, M. Ionicaa , P. Laricciaa,b, M. Magherinib,G. Mantovania,b, V. Mariania,b, M. Menichellia , A. Morozzia,b, F. Moscatellia,48 ,D. Passeria,b , A. Piccinellia,b , P. Placidia ,b , M. Presillaa,b , A. Rossia,b ,A. Santocchiaa,b , D. Spigaa , L. Storchia , T. Tedeschia,b , C. Turrioni

INFN Sezione di Pisa a, Pisa, Italy, Universita di Pisa b, Pisa, Italy, Scuola Normale Superioredi Pisa c, Pisa, Italy, Universita di Siena d, Siena, ItalyP. Azzurria , G. Bagliesia , A. Bastia , R. Beccherle , V. Bertacchia,c , L. Bianchinia ,T. Boccalia , F. Bosia, E. Bossinia,b , R. Castaldia , M.A. Cioccia,b , V. D’Amantea ,d ,R. Dell’Orsoa , M.R. Di Domenicoa ,d , S. Donatoa , A. Giassia , M.T. Grippoa ,F. Ligabuea ,c , G. Magazzua , E. Mancaa,c , G. Mandorlia,c , M. Massaa, E. Mazzonia ,A. Messineoa,b , A. Moggia , F. Morsania , F. Pallaa , S. Paroliaa,b, F. Raffaellia ,G. Ramirez-Sancheza,c, A. Rizzia,b , G. Rolandia,c , S. Roy Chowdhurya,c, A. Scribanoa,N. Shafieia,b , P. Spagnoloa , R. Tenchinia , G. Tonellia,b , N. Turinia,d , A. Venturia ,P.G. Verdinia

INFN Sezione di Roma a, Rome, Italy, Sapienza Universita di Roma b, Rome, ItalyP. Barriaa , M. Campanaa,b, F. Cavallaria , D. Del Rea ,b , E. Di Marcoa , M. Diemoza ,E. Longoa,b , P. Meridiania , G. Organtinia,b , F. Pandolfia, R. Paramattia,b ,C. Quarantaa,b, S. Rahatloua,b , C. Rovellia , F. Santanastasioa,b , L. Soffia ,R. Tramontanoa,b

INFN Sezione di Torino a, Torino, Italy, Universita di Torino b, Torino, Italy, Universita delPiemonte Orientale c, Novara, Italy

https://orcid.org/0000-0003-1124-8450

https://orcid.org/0000-0001-5490-605X

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https://orcid.org/0000-0001-5269-8517

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https://orcid.org/0000-0002-0227-1301

https://orcid.org/0000-0002-3086-8260

https://orcid.org/0000-0002-7497-7450

https://orcid.org/0000-0002-0218-4910

https://orcid.org/0000-0002-0782-0883

https://orcid.org/0000-0002-9918-1686

https://orcid.org/0000-0002-8387-762X

https://orcid.org/0000-0003-2461-275X

https://orcid.org/0000-0003-2414-4175

https://orcid.org/0000-0001-8219-2074

https://orcid.org/0000-0002-0098-2716

https://orcid.org/0000-0001-6262-4685

https://orcid.org/0000-0003-3072-1020

https://orcid.org/0000-0001-9526-556X

https://orcid.org/0000-0003-1327-9058

https://orcid.org/0000-0002-9258-1345

https://orcid.org/0000-0001-9821-4151

https://orcid.org/0000-0002-3798-1135

https://orcid.org/0000-0001-6471-5492

https://orcid.org/0000-0002-8233-7277

https://orcid.org/0000-0002-8773-4781

https://orcid.org/0000-0002-0807-8772

https://orcid.org/0000-0002-8412-4072

https://orcid.org/0000-0002-3129-828X

https://orcid.org/0000-0002-2205-5737

https://orcid.org/0000-0002-2359-8477

https://orcid.org/0000-0002-5360-1454

https://orcid.org/0000-0003-3474-2099

https://orcid.org/0000-0001-7915-1650

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https://orcid.org/0000-0002-1659-8727

https://orcid.org/0000-0001-8086-2863

https://orcid.org/0000-0002-1315-563X

https://orcid.org/0000-0002-7253-2669

https://orcid.org/0000-0003-3233-5123

https://orcid.org/0000-0001-8791-7978

https://orcid.org/0000-0003-1797-4330

https://orcid.org/0000-0002-5861-8140

https://orcid.org/0000-0002-1118-6205

https://orcid.org/0000-0001-7002-2051

https://orcid.org/0000-0002-8279-2464

https://orcid.org/0000-0002-4640-6108

https://orcid.org/0000-0003-4050-1769

https://orcid.org/0000-0003-4281-4582

https://orcid.org/0000-0003-3953-5996

https://orcid.org/0000-0003-0380-1172

https://orcid.org/0000-0003-3075-2679

https://orcid.org/0000-0002-9606-5604

https://orcid.org/0000-0002-7104-257X

https://orcid.org/0000-0001-5499-7916

https://orcid.org/0000-0001-5559-0894

https://orcid.org/0000-0003-1906-1076

https://orcid.org/0000-0003-0697-3420

https://orcid.org/0000-0003-0165-3962

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https://orcid.org/0000-0003-3977-6976

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https://orcid.org/0000-0002-9007-629X

https://orcid.org/0000-0001-8040-4993

https://orcid.org/0000-0002-9004-735X

https://orcid.org/0000-0002-7676-3106

https://orcid.org/0000-0001-5322-2414

https://orcid.org/0000-0003-0386-0527

https://orcid.org/0000-0002-5408-5180

https://orcid.org/0000-0003-2808-7315

https://orcid.org/0000-0002-2031-2955

https://orcid.org/0000-0002-9770-2249

https://orcid.org/0000-0002-2991-6384

https://orcid.org/0000-0001-5021-7759

https://orcid.org/0000-0002-7125-2905

https://orcid.org/0000-0002-1717-5654

https://orcid.org/0000-0003-4298-1620

https://orcid.org/0000-0003-2895-9638

https://orcid.org/0000-0003-2421-1171

https://orcid.org/0000-0001-9971-1176

https://orcid.org/0000-0002-6598-6865

https://orcid.org/0000-0002-9930-9299

https://orcid.org/0000-0002-2303-2588

https://orcid.org/0000-0003-0146-845X

https://orcid.org/0000-0003-0002-5462

https://orcid.org/0000-0002-7342-2592

https://orcid.org/0000-0003-1414-9343

https://orcid.org/0000-0002-7138-7017

https://orcid.org/0000-0001-7646-4977

https://orcid.org/0000-0001-9428-2296

https://orcid.org/0000-0002-4560-1614

https://orcid.org/0000-0002-1549-7107

https://orcid.org/0000-0002-1251-3597

https://orcid.org/0000-0001-8946-655X

https://orcid.org/0000-0002-5183-9020

https://orcid.org/0000-0002-3885-3821

https://orcid.org/0000-0001-7551-5613

https://orcid.org/0000-0002-2323-8017

https://orcid.org/0000-0001-7213-3214

https://orcid.org/0000-0002-6361-438X

https://orcid.org/0000-0001-5266-6865

https://orcid.org/0000-0002-4543-2718

https://orcid.org/0000-0002-0635-274X

https://orcid.org/0000-0002-8243-371X

https://orcid.org/0000-0001-7962-5203

https://orcid.org/0000-0003-2574-4383

https://orcid.org/0000-0003-2606-9156

https://orcid.org/0000-0002-9395-5230

https://orcid.org/0000-0002-0249-4142

https://orcid.org/0000-0002-0042-9507

https://orcid.org/0000-0002-3924-7380

https://orcid.org/0000-0002-1061-3877

https://orcid.org/0000-0003-0870-5796

https://orcid.org/0000-0002-5920-2438

https://orcid.org/0000-0002-3810-8530

https://orcid.org/0000-0001-6238-6787

https://orcid.org/0000-0002-8480-2259

https://orcid.org/0000-0002-3229-0781

https://orcid.org/0000-0002-0080-9550

https://orcid.org/0000-0001-9794-3360

https://orcid.org/0000-0003-2173-7530

https://orcid.org/0000-0003-2505-8359

https://orcid.org/0000-0003-2532-9876

72

N. Amapanea ,b , R. Arcidiaconoa,c , S. Argiroa,b , M. Arneodoa,c , N. Bartosika ,R. Bellana,b , A. Belloraa ,b , J. Berenguer Antequeraa,b , C. Biinoa , N. Cartigliaa ,S. Coli, S. Comettia , M. Costaa,b , R. Covarellia ,b , G. Dellacasaa , N. Demariaa ,S. Garbolinoa , M. Grippoa ,b, B. Kiania,b , F. Leggera , C. Mariottia , S. Masellia ,E. Migliorea,b , E. Monteila,b , M. Montenoa , M.M. Obertinoa,b , G. Ortonaa ,L. Pachera,b , N. Pastronea , M. Pelliccionia , G.L. Pinna Angionia,b, A. Rivettia,M. Ruspaa,c , K. Shchelinaa , F. Sivieroa,b , V. Solaa , A. Solanoa,b , D. Soldia,b ,A. Staianoa , M. Tornagoa,b, D. Trocinoa , A. Vagnerinia,b

INFN Sezione di Trieste a, Trieste, Italy, Universita di Trieste b, Trieste, ItalyS. Belfortea , V. Candelisea,b , M. Casarsaa , F. Cossuttia , A. Da Rolda,b ,G. Della Riccaa,b , G. Sorrentinoa,b, F. Vazzolera,b

Kyungpook National University, Daegu, KoreaS. Dogra , C. Huh , B. Kim, D.H. Kim , G.N. Kim , J. Kim, J. Lee, S.W. Lee ,C.S. Moon , Y.D. Oh , S.I. Pak, B.C. Radburn-Smith, S. Sekmen , Y.C. Yang

Chonnam National University, Institute for Universe and Elementary Particles, Kwangju,KoreaH. Kim , D.H. Moon

Hanyang University, Seoul, KoreaB. Francois , T.J. Kim , J. Park

Korea University, Seoul, KoreaS. Cho, S. Choi , Y. Go, B. Hong , K. Lee, K.S. Lee , J. Lim, J. Park, S.K. Park, J. Yoo

Kyung Hee University, Department of Physics, Seoul, Republic of Korea, Seoul, KoreaJ. Goh , A. Gurtu

Sejong University, Seoul, KoreaH.S. Kim , Y. Kim

Seoul National University, Seoul, KoreaJ. Almond, J.H. Bhyun, J. Choi, S. Jeon, J. Kim, J.S. Kim, S. Ko, H. Kwon, H. Lee , S. Lee,B.H. Oh, M. Oh , S.B. Oh, H. Seo , U.K. Yang, I. Yoon

University of Seoul, Seoul, KoreaW. Jang, D.Y. Kang, Y. Kang, S. Kim, B. Ko, J.S.H. Lee , Y. Lee, I.C. Park, Y. Roh, M.S. Ryu,D. Song, I.J. Watson , S. Yang

Yonsei University, Department of Physics, Seoul, KoreaS. Ha, H.D. Yoo

Sungkyunkwan University, Suwon, KoreaM. Choi, H. Lee, Y. Lee, I. Yu

College of Engineering and Technology, American University of the Mid-dle East (AUM), Egaila, Kuwait, Dasman, KuwaitT. Beyrouthy, Y. Maghrbi

Riga Technical University, Riga, LatviaV. Veckalns49

Vilnius University, Vilnius, LithuaniaM. Ambrozas, A. Carvalho Antunes De Oliveira , A. Juodagalvis , A. Rinkevicius ,

https://orcid.org/0000-0001-9449-2509

https://orcid.org/0000-0001-5904-142X

https://orcid.org/0000-0003-2150-3750

https://orcid.org/0000-0002-7790-7132

https://orcid.org/0000-0002-7196-2237

https://orcid.org/0000-0002-2539-2376

https://orcid.org/0000-0002-2753-5473

https://orcid.org/0000-0003-3153-0891

https://orcid.org/0000-0002-1397-7246

https://orcid.org/0000-0002-0548-9189

https://orcid.org/0000-0001-6621-7606

https://orcid.org/0000-0003-0156-0790

https://orcid.org/0000-0003-1216-5235

https://orcid.org/0000-0001-9873-4683

https://orcid.org/0000-0003-0743-9465

https://orcid.org/0000-0001-5604-1395

https://orcid.org/0000-0001-6431-5464

https://orcid.org/0000-0003-1400-0709

https://orcid.org/0000-0002-6864-3294

https://orcid.org/0000-0001-9871-7859

https://orcid.org/0000-0002-2271-5192

https://orcid.org/0000-0002-2350-213X

https://orcid.org/0000-0002-3521-6333

https://orcid.org/0000-0002-8781-8192

https://orcid.org/0000-0001-8411-2971

https://orcid.org/0000-0003-1288-4838

https://orcid.org/0000-0001-7291-1979

https://orcid.org/0000-0003-4728-6678

https://orcid.org/0000-0002-7655-3475

https://orcid.org/0000-0003-3742-0693

https://orcid.org/0000-0002-4427-4076

https://orcid.org/0000-0001-6288-951X

https://orcid.org/0000-0002-2971-8214

https://orcid.org/0000-0001-9059-4831

https://orcid.org/0000-0003-1803-624X

https://orcid.org/0000-0002-2830-5872

https://orcid.org/0000-0001-8443-4460

https://orcid.org/0000-0002-3641-5983

https://orcid.org/0000-0002-1353-8964

https://orcid.org/0000-0001-5672-214X

https://orcid.org/0000-0003-0342-7977

https://orcid.org/0000-0003-2831-6982

https://orcid.org/0000-0001-8111-9318

https://orcid.org/0000-0002-0812-0758

https://orcid.org/0000-0002-8513-2824

https://orcid.org/0000-0002-9023-6847

https://orcid.org/0000-0002-3482-9082

https://orcid.org/0000-0002-1028-3468

https://orcid.org/0000-0001-8229-7829

https://orcid.org/0000-0002-7219-9931

https://orcid.org/0000-0003-1726-5681

https://orcid.org/0000-0001-8019-9387

https://orcid.org/0000-0002-5628-9187

https://orcid.org/0000-0002-2190-9059

https://orcid.org/0000-0001-8336-2434

https://orcid.org/0000-0002-4683-6669

https://orcid.org/0000-0001-6225-9876

https://orcid.org/0000-0002-2259-9929

https://orcid.org/0000-0002-3680-7039

https://orcid.org/0000-0002-1129-2083

https://orcid.org/0000-0002-6543-9191

https://orcid.org/0000-0002-1138-3700

https://orcid.org/0000-0003-2618-9203

https://orcid.org/0000-0002-3932-0605

https://orcid.org/0000-0002-3491-8026

https://orcid.org/0000-0002-2153-1519

https://orcid.org/0000-0003-2141-3413

https://orcid.org/0000-0003-1567-5548

https://orcid.org/0000-0003-3676-9711

https://orcid.org/0000-0003-2340-836X

https://orcid.org/0000-0002-1501-3328

https://orcid.org/0000-0002-7510-255X

73

G. Tamulaitis

National Centre for Particle Physics, Universiti Malaya, Kuala Lumpur, MalaysiaN. Bin Norjoharuddeen , W.A.T. Wan Abdullah, M.N. Yusli, Z. Zolkapli

Universidad de Sonora (UNISON), Hermosillo, MexicoJ.F. Benitez , A. Castaneda Hernandez , M. Leon Coello, J.A. Murillo Quijada ,A. Sehrawat, L. Valencia Palomo

Centro de Investigacion y de Estudios Avanzados del IPN, Mexico City, MexicoG. Ayala, H. Castilla-Valdez, E. De La Cruz-Burelo , I. Heredia-De La Cruz50 , R. Lopez-Fernandez, C.A. Mondragon Herrera, D.A. Perez Navarro, A. Sanchez Hernandez

Universidad Iberoamericana, Mexico City, MexicoS. Carrillo Moreno, C. Oropeza Barrera , F. Vazquez Valencia

Benemerita Universidad Autonoma de Puebla, Puebla, MexicoI. Pedraza, H.A. Salazar Ibarguen, C. Uribe Estrada

University of Montenegro, Podgorica, MontenegroJ. Mijuskovic51, N. Raicevic

University of Auckland, Auckland, New ZealandD. Krofcheck

University of Canterbury, Christchurch, New ZealandP.H. Butler

National Centre for Physics, Quaid-I-Azam University, Islamabad, PakistanA. Ahmad, M.I. Asghar, A. Awais, M.I.M. Awan, H.R. Hoorani, W.A. Khan, M.A. Shah,M. Shoaib , M. Waqas

AGH University of Science and Technology Faculty of Computer Science, Electron-ics and Telecommunications, Krakow, PolandV. Avati, L. Grzanka, M. Malawski

National Centre for Nuclear Research, Swierk, PolandH. Bialkowska, M. Bluj , B. Boimska , M. Gorski, M. Kazana, M. Szleper , P. Zalewski

Institute of Experimental Physics, Faculty of Physics, University of Warsaw, Warsaw, PolandK. Bunkowski, K. Doroba, A. Kalinowski , M. Konecki , J. Krolikowski

Laboratorio de Instrumentacao e Fısica Experimental de Partıculas, Lisboa, PortugalM. Araujo, P. Bargassa , D. Bastos, A. Boletti , P. Faccioli , M. Gallinaro , J. Hollar ,N. Leonardo , T. Niknejad, M. Pisano, J. Seixas , O. Toldaiev , J. Varela

Joint Institute for Nuclear Research, Dubna, RussiaS. Afanasiev, D. Budkouski, I. Golutvin, I. Gorbunov , V. Karjavine, V. Korenkov , A. Lanev,A. Malakhov, V. Matveev52,53, V. Palichik, V. Perelygin, M. Savina, D. Seitova, V. Shalaev,S. Shmatov, S. Shulha, V. Smirnov, O. Teryaev, N. Voytishin, B.S. Yuldashev54, A. Zarubin,I. Zhizhin

Petersburg Nuclear Physics Institute, Gatchina (St. Petersburg), RussiaG. Gavrilov , V. Golovtcov, Y. Ivanov, V. Kim55 , E. Kuznetsova56, V. Murzin, V. Oreshkin,I. Smirnov, D. Sosnov , V. Sulimov, L. Uvarov, S. Volkov, A. Vorobyev

Institute for Nuclear Research, Moscow, Russia

https://orcid.org/0000-0002-2913-9634

https://orcid.org/0000-0002-8818-7476

https://orcid.org/0000-0002-2633-6712

https://orcid.org/0000-0003-4766-1546

https://orcid.org/0000-0003-4933-2092

https://orcid.org/0000-0002-8736-440X

https://orcid.org/0000-0002-7469-6974

https://orcid.org/0000-0002-8133-6467

https://orcid.org/0000-0001-9548-0358

https://orcid.org/0000-0001-9724-0016

https://orcid.org/0000-0001-5494-7302

https://orcid.org/0000-0001-9878-2140

https://orcid.org/0000-0001-6791-8252

https://orcid.org/0000-0002-3846-9483

https://orcid.org/0000-0003-1229-1442

https://orcid.org/0000-0002-4200-1541

https://orcid.org/0000-0002-1697-004X

https://orcid.org/0000-0002-1280-5493

https://orcid.org/0000-0001-9482-4841

https://orcid.org/0000-0002-3055-0236

https://orcid.org/0000-0001-8612-3332

https://orcid.org/0000-0003-3288-7737

https://orcid.org/0000-0003-1849-6692

https://orcid.org/0000-0003-1261-2277

https://orcid.org/0000-0002-8664-0134

https://orcid.org/0000-0002-9746-4594

https://orcid.org/0000-0002-7531-0842

https://orcid.org/0000-0002-8286-8780

https://orcid.org/0000-0003-2613-3146

https://orcid.org/0000-0003-3777-6606

https://orcid.org/0000-0002-2342-7862

https://orcid.org/0000-0003-3968-0253

https://orcid.org/0000-0001-7161-2133

https://orcid.org/0000-0002-7452-8380

74

Yu. Andreev , A. Dermenev, S. Gninenko , N. Golubev, A. Karneyeu , D. Kirpichnikov ,M. Kirsanov, N. Krasnikov, A. Pashenkov, G. Pivovarov , A. Toropin

Institute for Theoretical and Experimental Physics named by A.I. Alikhanov of NRC ‘Kur-chatov Institute’, Moscow, RussiaV. Epshteyn, V. Gavrilov, N. Lychkovskaya, A. Nikitenko57, V. Popov, A. Stepennov, M. Toms,E. Vlasov , A. Zhokin

Moscow Institute of Physics and Technology, Moscow, RussiaT. Aushev

National Research Nuclear University ’Moscow Engineering Physics Institute’ (MEPhI),Moscow, RussiaO. Bychkova, M. Chadeeva58 , P. Parygin, E. Popova, V. Rusinov, D. Selivanova

P.N. Lebedev Physical Institute, Moscow, RussiaV. Andreev, M. Azarkin, I. Dremin , M. Kirakosyan, A. Terkulov

Skobeltsyn Institute of Nuclear Physics, Lomonosov Moscow State University, Moscow,RussiaA. Belyaev, E. Boos , M. Dubinin59 , L. Dudko , A. Ershov, A. Gribushin, A. Kaminskiy60,V. Klyukhin , O. Kodolova , I. Lokhtin , S. Obraztsov, S. Petrushanko, V. Savrin

Novosibirsk State University (NSU), Novosibirsk, RussiaV. Blinov61, T. Dimova61, L. Kardapoltsev61, A. Kozyrev61, I. Ovtin61, O. Radchenko61,Y. Skovpen61

Institute for High Energy Physics of National Research Centre ‘Kurchatov Institute’,Protvino, RussiaI. Azhgirey , I. Bayshev, D. Elumakhov, V. Kachanov, D. Konstantinov , P. Mandrik ,V. Petrov, R. Ryutin, S. Slabospitskii , A. Sobol, S. Troshin , N. Tyurin, A. Uzunian, A. Volkov

National Research Tomsk Polytechnic University, Tomsk, RussiaA. Babaev, V. Okhotnikov

Tomsk State University, Tomsk, RussiaV. Borshch, V. Ivanchenko , E. Tcherniaev

University of Belgrade: Faculty of Physics and VINCA Institute of Nuclear Sciences,Belgrade, SerbiaP. Adzic62 , M. Dordevic , P. Milenovic , J. Milosevic

Centro de Investigaciones Energeticas Medioambientales y Tecnologicas (CIEMAT),Madrid, SpainM. Aguilar-Benitez, J. Alcaraz Maestre , A. Alvarez Fernandez, I. Bachiller, M. Barrio Luna,Cristina F. Bedoya , C.A. Carrillo Montoya , M. Cepeda , M. Cerrada, N. Colino ,B. De La Cruz, A. Delgado Peris , J.P. Fernandez Ramos , J. Flix , M.C. Fouz , O. Gon-zalez Lopez , S. Goy Lopez , J.M. Hernandez , M.I. Josa , J. Leon Holgado , D. Moran,A. Navarro Tobar , C. Perez Dengra, A. Perez-Calero Yzquierdo , J. Puerta Pelayo ,I. Redondo , L. Romero, S. Sanchez Navas, L. Urda Gomez , C. Willmott

Universidad Autonoma de Madrid, Madrid, SpainJ.F. de Troconiz, R. Reyes-Almanza

Universidad de Oviedo, Instituto Universitario de Ciencias y Tecnologıas Espaciales de As-turias (ICTEA), Oviedo, Spain

https://orcid.org/0000-0002-7397-9665

https://orcid.org/0000-0001-6495-7619

https://orcid.org/0000-0001-9983-1004

https://orcid.org/0000-0002-7177-077X

https://orcid.org/0000-0001-6435-4463

https://orcid.org/0000-0002-8628-2090

https://orcid.org/0000-0003-1814-1218

https://orcid.org/0000-0001-7451-247X

https://orcid.org/0000-0002-0193-5073

https://orcid.org/0000-0002-7766-7175

https://orcid.org/0000-0002-4462-3192

https://orcid.org/0000-0002-8577-6531

https://orcid.org/0000-0003-1342-4251

https://orcid.org/0000-0002-4457-8678

https://orcid.org/0000-0002-3316-0604

https://orcid.org/0000-0003-0528-341X

https://orcid.org/0000-0001-6673-7273

https://orcid.org/0000-0001-5197-046X

https://orcid.org/0000-0001-8178-2494

https://orcid.org/0000-0001-5493-1773

https://orcid.org/0000-0002-1844-5433

https://orcid.org/0000-0002-3685-0635

https://orcid.org/0000-0002-5862-7397

https://orcid.org/0000-0002-8407-3236

https://orcid.org/0000-0001-7132-3550

https://orcid.org/0000-0001-8486-4604

https://orcid.org/0000-0003-0914-7474

https://orcid.org/0000-0001-8057-9152

https://orcid.org/0000-0002-6245-6535

https://orcid.org/0000-0002-6076-4083

https://orcid.org/0000-0002-3656-0259

https://orcid.org/0000-0002-8511-7958

https://orcid.org/0000-0002-0122-313X

https://orcid.org/0000-0003-2688-8047

https://orcid.org/0000-0003-2950-976X

https://orcid.org/0000-0002-4532-6464

https://orcid.org/0000-0001-6508-5090

https://orcid.org/0000-0001-6436-7547

https://orcid.org/0000-0002-4985-6964

https://orcid.org/0000-0002-4156-6460

https://orcid.org/0000-0003-3606-1780

https://orcid.org/0000-0003-3036-7965

https://orcid.org/0000-0001-7390-1457

https://orcid.org/0000-0003-3737-4121

https://orcid.org/0000-0002-7865-5010

https://orcid.org/0000-0002-4600-7772

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B. Alvarez Gonzalez , J. Cuevas , C. Erice , J. Fernandez Menendez , S. Folgueras ,I. Gonzalez Caballero , J.R. Gonzalez Fernandez, E. Palencia Cortezon , C. Ramon Alvarez,V. Rodrıguez Bouza , A. Soto Rodrıguez, A. Trapote, N. Trevisani , C. Vico Villalba

Instituto de Fısica de Cantabria (IFCA), CSIC-Universidad de Cantabria, Santander, SpainJ.A. Brochero Cifuentes , I.J. Cabrillo, A. Calderon , E. Curras, J. Duarte Campderros ,M. Fernandez , C. Fernandez Madrazo , P.J. Fernandez Manteca , A. Garcıa Alonso,G. Gomez, J. Gonzalez Sanchez, R.W. Jaramillo Echeverria, C. Martinez Rivero, P. Mar-tinez Ruiz del Arbol , F. Matorras , P. Matorras Cuevas , D. Moya, J. Piedra Gomez ,C. Prieels, T. Rodrigo , A. Ruiz-Jimeno , L. Scodellaro , I. Vila, A.L. Virto, J.M. Vizan Gar-cia

University of Colombo, Colombo, Sri LankaM.K. Jayananda, B. Kailasapathy63, D.U.J. Sonnadara, D.D.C. Wickramarathna

University of Ruhuna, Department of Physics, Matara, Sri LankaW.G.D. Dharmaratna , K. Liyanage, N. Perera, N. Wickramage

CERN, European Organization for Nuclear Research, Geneva, SwitzerlandT.K. Aarrestad , D. Abbaneo, E. Albert, J. Alimena , E. Auffray, G. Auzinger, J. Baechler,P. Baillon†, M. Barinoff, D. Barney , J. Batista Lopes, J. Bendavid, G. Bergamin, M. Bianco ,G. Blanchot, A. Bocci , F. Boyer, T. Camporesi, M. Capeans Garrido , A. Caratelli ,R. Carnesecchi, D. Ceresa, G. Cerminara, N. Chernyavskaya , S.S. Chhibra , J. Christiansen,K. Cichy, M. Cipriani , L. Cristella , D. d’Enterria , A. Dabrowski , J. Daguin,A. David , A. De Roeck , M.M. Defranchis , M. Deile , S. Detraz, M. Dobson, M. Dudek,M. Dunser , N. Dupont, A. Elliott-Peisert, N. Emriskova, F. Fallavollita64, D. Fasanella ,J.P. Figueiredo De Sa Sousa De Almeida, A. Filenius, A. Florent , N. Frank, G. Franzoni ,T. French, W. Funk, S. Giani, D. Gigi, K. Gill, F. Glege, L. Gouskos , M. Haranko ,J. Hegeman , A.E. Hollos, G. Hugo , V. Innocente , T. James, P. Janot , J. Kaplon,J. Kaspar , Z. Kerekes, J. Kieseler , K. Kloukinas, M. Komm , N. Koss, L.J. Kottelat,M.I. Kovacs, N. Kratochwil, A. La Rosa , C. Lange , S. Laurila, P. Lecoq , P. Lenoir,A. Lintuluoto, K. Long , R. Loos, C. Lourenco , B. Maier, L. Malgeri , S. Mallios,M. Mannelli, A. Marchioro, A.C. Marini , I. Mateos Domınguez, F. Meijers, S. Mersi ,E. Meschi , S. Michelis, A. Millet, F. Moortgat , M. Mulders , A. Onnela, S. Orfanelli,L. Orsini, T. Pakulski, F. Pantaleo , L. Pape, A. Perez, E. Perez, F. Perez Gomez, J.F. Pernot,M. Peruzzi , P. Petagna, A. Petrilli, G. Petrucciani , A. Pfeiffer , Q. Piazza, M. Pierini ,D. Piparo, M. Pitt , H. Postema, H. Qu , T. Quast, D. Rabady , A. Racz, G. Reales Gutierrez,M. Rieger , P. Rose, M. Rovere, H. Sakulin, J. Salfeld-Nebgen , S. Scarfi, C. Schafer,C. Schwick, M. Selvaggi , A. Sharma, P. Silva , M. Sinani, W. Snoeys , P. Sphicas65 ,S. Summers , K. Tatar , R. Tavares Rego, V.R. Tavolaro , D. Treille, P. Tropea, J. Troska ,A. Tsirou, G.P. Van Onsem , F. Vasey , P. Vichoudis, J. Wanczyk66, K.A. Wozniak,W.D. Zeuner

Paul Scherrer Institut, Villigen, SwitzerlandW. Bertl†, L. Caminada67 , A. Ebrahimi , W. Erdmann, R. Horisberger, Q. Ingram,H.C. Kaestli, D. Kotlinski, U. Langenegger, B. Meier, M. Missiroli67 , L. Noehte67, T. Rohe,S. Streuli

ETH Zurich - Institute for Particle Physics and Astrophysics (IPA), Zurich, SwitzerlandK. Androsov66 , M. Backhaus , R. Becker, P. Berger, A. Calandri ,D.R. Da Silva Di Calafiori, A. De Cosa, G. Dissertori , M. Dittmar, L. Djambazov,M. Donega, C. Dorfer , F. Eble, K. Gedia, F. Glessgen, T.A. Gomez Espinosa , C. Grab ,

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https://orcid.org/0000-0003-2093-7856

https://orcid.org/0000-0002-7205-2040

https://orcid.org/0000-0003-0687-5214

https://orcid.org/0000-0002-4824-1087

https://orcid.org/0000-0001-9748-4336

https://orcid.org/0000-0003-2566-7496

https://orcid.org/0000-0002-7737-5121

https://orcid.org/0000-0003-4295-5668

https://orcid.org/0000-0001-7481-7273

https://orcid.org/0000-0002-9157-1700

https://orcid.org/0000-0002-4795-195X

https://orcid.org/0000-0002-3639-0368

https://orcid.org/0000-0002-4974-8330

https://orcid.org/0000-0002-6823-8854

https://orcid.org/0000-0002-6366-837X

https://orcid.org/0000-0002-7671-243X

https://orcid.org/0000-0001-6030-3191

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https://orcid.org/0000-0002-8336-3282

https://orcid.org/0000-0002-6515-5666

https://orcid.org/0000-0001-7727-9175

https://orcid.org/0000-0002-4203-9339

https://orcid.org/0000-0002-2264-2229

https://orcid.org/0000-0002-1643-1388

https://orcid.org/0000-0002-0151-4439

https://orcid.org/0000-0002-4279-1221

https://orcid.org/0000-0002-5754-4303

https://orcid.org/0000-0003-2570-9676

https://orcid.org/0000-0001-5854-7699

https://orcid.org/0000-0002-9228-5271

https://orcid.org/0000-0001-9573-3714

https://orcid.org/0000-0001-5085-7270

https://orcid.org/0000-0002-8502-2297

https://orcid.org/0000-0002-2926-2691

https://orcid.org/0000-0001-6544-3679

https://orcid.org/0000-0001-9179-4253

https://orcid.org/0000-0002-9547-7471

https://orcid.org/0000-0002-9376-9235

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https://orcid.org/0000-0003-2232-5407

https://orcid.org/0000-0003-3209-2088

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https://orcid.org/0000-0003-0885-6711

https://orcid.org/0000-0002-0113-7389

https://orcid.org/0000-0003-2351-0487

https://orcid.org/0000-0003-2155-6692

https://orcid.org/0000-0003-4502-6151

https://orcid.org/0000-0001-7199-0046

https://orcid.org/0000-0001-7432-6634

https://orcid.org/0000-0003-3266-4357

https://orcid.org/0000-0002-0416-696X

https://orcid.org/0000-0003-0889-4726

https://orcid.org/0000-0001-5328-448X

https://orcid.org/0000-0003-1939-4268

https://orcid.org/0000-0003-2461-5985

https://orcid.org/0000-0002-0250-8655

https://orcid.org/0000-0001-9239-0605

https://orcid.org/0000-0003-0797-2606

https://orcid.org/0000-0003-3879-5622

https://orcid.org/0000-0002-5144-9655

https://orcid.org/0000-0002-5725-041X

https://orcid.org/0000-0003-3541-9066

https://orcid.org/0000-0002-5456-5977

https://orcid.org/0000-0003-4244-2061

https://orcid.org/0000-0002-6448-0168

https://orcid.org/0000-0003-2518-7521

https://orcid.org/0000-0002-0707-5051

https://orcid.org/0000-0002-1664-2337

https://orcid.org/0000-0002-4360-5259

https://orcid.org/0000-0001-5677-6033

https://orcid.org/0000-0003-4472-867X

https://orcid.org/0000-0002-1780-1344

https://orcid.org/0000-0003-2694-6542

https://orcid.org/0000-0002-5888-2304

https://orcid.org/0000-0001-7774-0099

https://orcid.org/0000-0002-4549-2569

https://orcid.org/0000-0002-2163-442X

https://orcid.org/0000-0002-9443-7769

https://orcid.org/0000-0002-6182-3380

76

D. Hits, W. Lustermann, A.-M. Lyon, R.A. Manzoni , L. Marchese , C. Martin Perez,M.T. Meinhard, F. Nessi-Tedaldi, J. Niedziela , F. Pauss, V. Perovic, S. Pigazzini ,M.G. Ratti , M. Reichmann, C. Reissel, T. Reitenspiess, B. Ristic , U. Roser, D. Ruini,D.A. Sanz Becerra , J. Soerensen, V. Stampf, J. Steggemann66 , R. Wallny , D.H. Zhu

Universitat Zurich, Zurich, SwitzerlandC. Amsler68 , P. Bartschi, K. Bosiger, C. Botta , D. Brzhechko, M.F. Canelli , K. Cormier,A. De Wit , R. Del Burgo, J.K. Heikkila , M. Huwiler, W. Jin, A. Jofrehei , B. Kilminster ,S. Leontsinis , S.P. Liechti, A. Macchiolo , R. Maier, P. Meiring, V.M. Mikuni , U. Molinatti,I. Neutelings, A. Reimers, P. Robmann, S. Sanchez Cruz , K. Schweiger , Y. Takahashi ,D. Wolf

National Central University, Chung-Li, TaiwanC. Adloff69, C.M. Kuo, W. Lin, A. Roy , T. Sarkar39 , S.S. Yu

National Taiwan University (NTU), Taipei, TaiwanL. Ceard, Y. Chao, K.F. Chen , P.H. Chen , W.-S. Hou , Y.y. Li, R.-S. Lu, E. Paganis ,A. Psallidas, A. Steen, H.y. Wu, E. Yazgan , P.r. Yu

Chulalongkorn University, Faculty of Science, Department of Physics, Bangkok, ThailandB. Asavapibhop , C. Asawatangtrakuldee , N. Srimanobhas

Cukurova University, Physics Department, Science and Art Faculty, Adana, TurkeyF. Boran , S. Damarseckin70, Z.S. Demiroglu , F. Dolek , I. Dumanoglu71 , E. Eskut,Y. Guler72 , E. Gurpinar Guler72 , C. Isik, O. Kara, A. Kayis Topaksu, U. Kiminsu ,G. Onengut, K. Ozdemir73, A. Polatoz, A.E. Simsek , B. Tali74, U.G. Tok , S. Turkcapar,I.S. Zorbakir

Middle East Technical University, Physics Department, Ankara, TurkeyB. Isildak75, G. Karapinar76, K. Ocalan77 , M. Yalvac78

Bogazici University, Istanbul, TurkeyB. Akgun, I.O. Atakisi , E. Gulmez , M. Kaya79 , O. Kaya80, O. Ozcelik, S. Tekten81,E.A. Yetkin82

Istanbul Technical University, Istanbul, TurkeyA. Cakir , K. Cankocak71 , Y. Komurcu, S. Sen83

Istanbul University, Istanbul, TurkeyS. Cerci74, I. Hos84, B. Kaynak, S. Ozkorucuklu, D. Sunar Cerci74 , C. Zorbilmez

Institute for Scintillation Materials of National Academy of Science of Ukraine, Kharkov,UkraineB. Grynyov

National Scientific Center, Kharkov Institute of Physics and Technology, Kharkov, UkraineL. Levchuk

University of Bristol, Bristol, United KingdomD. Anthony, E. Bhal , S. Bologna, J.J. Brooke , A. Bundock , E. Clement , D. Cussans ,H. Flacher , J. Goldstein , G.P. Heath, H.F. Heath , L. Kreczko , B. Krikler ,S. Paramesvaran, S. Seif El Nasr-Storey, V.J. Smith, N. Stylianou85 , K. Walkingshaw Pass,R. White

Rutherford Appleton Laboratory, Didcot, United KingdomK.W. Bell, A. Belyaev86 , C. Brew , R.M. Brown, D.J.A. Cockerill, C. Cooke, J.A. Coughlan,

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https://orcid.org/0000-0002-0538-1469

https://orcid.org/0000-0002-8992-5426

https://orcid.org/0000-0002-6657-0407

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https://orcid.org/0000-0001-7977-7127

https://orcid.org/0000-0001-7092-5517

https://orcid.org/0000-0002-0039-5503

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https://orcid.org/0000-0002-6172-0285

https://orcid.org/0000-0001-6940-7800

https://orcid.org/0000-0002-9074-2256

https://orcid.org/0000-0002-3039-021X

https://orcid.org/0000-0002-5962-2221

https://orcid.org/0000-0002-8419-1400

https://orcid.org/0000-0003-4915-9162

https://orcid.org/0000-0002-9231-7464

https://orcid.org/0000-0002-6353-518X

https://orcid.org/0000-0003-2890-4493

https://orcid.org/0000-0002-9007-8260

https://orcid.org/0000-0002-8627-7689

https://orcid.org/0000-0002-3829-3481

https://orcid.org/0000-0001-7325-1087

https://orcid.org/0000-0002-5412-4688

https://orcid.org/0000-0001-5889-7410

https://orcid.org/0000-0003-4494-628X

https://orcid.org/0000-0002-6078-3348

https://orcid.org/0000-0002-2916-6456

https://orcid.org/0000-0003-3412-4004

https://orcid.org/0000-0001-8192-0826

https://orcid.org/0000-0002-5371-941X

https://orcid.org/0000-0003-1591-6014

https://orcid.org/0000-0001-6576-9740

https://orcid.org/0000-0003-2341-8330

https://orcid.org/0000-0001-9712-0030

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https://orcid.org/0000-0002-1733-4408

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77

K.V. Ellis, K. Harder, S. Harper, M.-L. Holmberg87, J. Linacre , K. Manolopoulos,D.M. Newbold , E. Olaiya, D. Petyt, T. Reis , T. Schuh, C.H. Shepherd-Themistocleous,I.R. Tomalin, T. Williams

Imperial College, London, United KingdomR. Bainbridge , P. Bloch , S. Bonomally, J. Borg , S. Breeze, C.E. Brown, O. Buchmuller,V. Cepaitis , G.S. Chahal88 , D. Colling, P. Dauncey , G. Davies , M. Della Negra ,S. Fayer, G. Fedi , G. Hall , M.H. Hassanshahi, G. Iles, J. Langford, L. Lyons, A.-M. Magnan, S. Malik, A. Martelli , D.G. Monk, J. Nash89 , M. Pesaresi, D.M. Raymond,A. Richards, A. Rose, E. Scott , C. Seez, A. Shtipliyski, A. Tapper , K. Uchida, T. Virdee18 ,M. Vojinovic , N. Wardle , S.N. Webb , D. Winterbottom

Brunel University, Uxbridge, United KingdomK. Coldham, J.E. Cole , M. Ghorbani, A. Khan, P. Kyberd , I.D. Reid , L. Teodorescu,S. Zahid

Baylor University, Waco, Texas, USAS. Abdullin , A. Brinkerhoff , B. Caraway , J. Dittmann , K. Hatakeyama ,A.R. Kanuganti, B. McMaster , N. Pastika, M. Saunders , S. Sawant, C. Sutantawibul,J. Wilson

Catholic University of America, Washington, DC, USAR. Bartek , A. Dominguez , R. Uniyal , A.M. Vargas Hernandez

The University of Alabama, Tuscaloosa, Alabama, USAA. Buccilli , S.I. Cooper , D. Di Croce , S.V. Gleyzer , C. Henderson , C.U. Perez ,P. Rumerio90 , C. West

Boston University, Boston, Massachusetts, USAA. Akpinar , A. Albert , D. Arcaro , C. Cosby , Z. Demiragli , E. Fontanesi, D. Gastler,E. Hazen, S. May , A. Peck, J. Rohlf , K. Salyer , D. Sperka, D. Spitzbart , I. Suarez ,A. Tsatsos, S. Yuan, D. Zou

Brown University, Providence, Rhode Island, USAG. Benelli , B. Burkle , X. Coubez23, D. Cutts , M. Hadley , U. Heintz , N. Hinton,J.M. Hogan91 , A. Honma , A. Korotkov, T. KWON, G. Landsberg , K.T. Lau , D. Li,M. Lukasik, J. Luo , M. Narain, N. Pervan, S. Sagir92 , F. Simpson, E. Spencer, E. Usai ,W.Y. Wong, X. Yan , D. Yu , W. Zhang

University of California, Davis, Davis, California, USAJ. Bonilla , C. Brainerd , R. Breedon, M. Calderon De La Barca Sanchez, E. Cannaert,M. Chertok , J. Conway , P.T. Cox, R. Erbacher, G. Haza, D. Hemer, F. Jensen , O. Kukral,R. Lander, M. Mulhearn , D. Pellett, B. Regnery , D. Taylor , J. Thomson, W. Wei, T. Welton,Y. Yao , F. Zhang

University of California, Los Angeles, California, USAM. Bachtis , R. Cousins , A. Datta , D. Hamilton, J. Hauser , M. Ignatenko, M.A. Iqbal,T. Lam, W.A. Nash, S. Regnard , D. Saltzberg , B. Stone, V. Valuev

University of California, Riverside, Riverside, California, USAK. Burt, Y. Chen, R. Clare , J.W. Gary , M. Gordon, G. Hanson , G. Karapostoli ,O.R. Long , N. Manganelli, M. Olmedo Negrete, W. Si , S. Wimpenny, Y. Zhang

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University of California, Santa Barbara - Department of Physics, Santa Barbara, California,USAN. Amin, C. Campagnari , M. Citron , A. Dorsett, V. Dutta , J. Incandela ,M. Kilpatrick , J. Kim , S. Kyre, B. Marsh, H. Mei, M. Oshiro, M. Quinnan , J. Richman,U. Sarica , F. Setti, J. Sheplock, D. Stuart, S. Wang

California Institute of Technology, Pasadena, California, USAA. Bornheim , O. Cerri, I. Dutta , J.M. Lawhorn , N. Lu , J. Mao, H.B. Newman ,T.Q. Nguyen , M. Spiropulu , J.R. Vlimant , C. Wang , S. Xie , Z. Zhang , R.Y. Zhu

Carnegie Mellon University, Pittsburgh, Pennsylvania, USAJ. Alison , S. An , M.B. Andrews, P. Bryant , T. Ferguson , A. Harilal, C. Liu,T. Mudholkar , M. Paulini , A. Sanchez, W. Terrill

University of Colorado Boulder, Boulder, Colorado, USAJ.P. Cumalat , W.T. Ford , A. Hassani, E. MacDonald, R. Patel, A. Perloff , C. Savard,K. Stenson , K.A. Ulmer , S.R. Wagner

Cornell University, Ithaca, New York, USAJ. Alexander , S. Bright-Thonney , Y. Cheng , J. Conway, D.J. Cranshaw , J. Fan,S. Hogan, S. Lantz, J. Monroy , Y. Padilla Fuentes, J.R. Patterson , D. Quach , J. Reichert ,M. Reid , D. Riley , A. Ryd, K. Smolenski, C. Strohman, W. Sun , J. Thom , P. Wittich ,R. Zou

Fermi National Accelerator Laboratory, Batavia, Illinois, USAM. Albrow , M. Alyari , G. Apollinari, A. Apresyan , A. Apyan , A. Bakshi, S. Banerjee,L.A.T. Bauerdick , D. Berry , J. Berryhill , P.C. Bhat, K. Burkett , D. Butler, J.N. Butler,A. Canepa, G.B. Cerati , H.W.K. Cheung , F. Chlebana, M. Cremonesi, G. Derylo,K.F. Di Petrillo , J. Dickinson , V.D. Elvira , Y. Feng, J. Freeman, Z. Gecse, A. Ghosh,C. Gingu , H. Gonzalez, L. Gray, D. Green, S. Grunendahl , O. Gutsche , R.M. Harris ,R. Heller, T.C. Herwig , J. Hirschauer , B. Jayatilaka , S. Jindariani, M. Johnson,U. Joshi, P. Klabbers , T. Klijnsma , B. Klima , K.H.M. Kwok, S. Lammel , C.M. Lei,D. Lincoln , R. Lipton, T. Liu, C. Madrid, K. Maeshima, C. Mantilla , D. Mason,P. McBride , P. Merkel, S. Mrenna , S. Nahn , J. Ngadiuba , V. O’Dell, V. Papadimitriou,K. Pedro , C. Pena59 , O. Prokofyev, F. Ravera , A. Reinsvold Hall , L. Ristori ,E. Sexton-Kennedy , N. Smith , A. Soha , W.J. Spalding , L. Spiegel, S. Stoynev ,J. Strait , L. Taylor , S. Tkaczyk, N.V. Tran , L. Uplegger , E.W. Vaandering , E. Voirin,H.A. Weber

University of Florida, Gainesville, Florida, USAD. Acosta , P. Avery, D. Bourilkov , L. Cadamuro , V. Cherepanov, F. Errico , R.D. Field,D. Guerrero, B.M. Joshi , M. Kim, E. Koenig, J. Konigsberg , A. Korytov, K.H. Lo,K. Matchev , N. Menendez , G. Mitselmakher , A. Muthirakalayil Madhu, N. Rawal,D. Rosenzweig, S. Rosenzweig, J. Rotter, K. Shi , J. Sturdy , J. Wang , E. Yigitbasi , X. Zuo

Florida State University, Tallahassee, Florida, USAT. Adams , A. Askew , R. Habibullah , V. Hagopian, K.F. Johnson, R. Khurana,T. Kolberg , G. Martinez, H. Prosper , C. Schiber, O. Viazlo , R. Yohay , J. Zhang

Florida Institute of Technology, Melbourne, Florida, USA

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79

M.M. Baarmand , S. Butalla, T. Elkafrawy93 , M. Hohlmann , R. Kumar Verma ,D. Noonan , M. Rahmani, F. Yumiceva

University of Illinois at Chicago (UIC), Chicago, Illinois, USAM.R. Adams, H. Becerril Gonzalez , R. Cavanaugh , X. Chen , S. Dittmer, A. Evdoki-mov , O. Evdokimov , C.E. Gerber , D.A. Hangal , D.J. Hofman , A.H. Merrit,C. Mills , G. Oh , T. Roy, S. Rudrabhatla, M.B. Tonjes , N. Varelas , J. Viinikainen ,X. Wang, Z. Wu , Z. Ye , J. Yoo

The University of Iowa, Iowa City, Iowa, USAM. Alhusseini , K. Dilsiz94 , S. Durgut, R.P. Gandrajula , O.K. Koseyan , J.-P. Merlo,A. Mestvirishvili95, J. Nachtman, H. Ogul96 , Y. Onel , A. Penzo, C. Rude, C. Snyder,E. Tiras97

Johns Hopkins University, Baltimore, Maryland, USAO. Amram , B. Blumenfeld , L. Corcodilos , J. Davis, V. De Havenon, M. Eminizer ,J. Feingold, A.V. Gritsan , S. Kyriacou, P. Maksimovic , C. Martin , J. Roskes ,K. Sullivan, M. Swartz, T.A. Vami , C. You

The University of Kansas, Lawrence, Kansas, USAA. Abreu, J. Anguiano, C. Baldenegro Barrera , P. Baringer , A. Bean , A. Bylinkin ,Z. Flowers, T. Isidori, S. Khalil , J. King, G. Krintiras , A. Kropivnitskaya , M. Lazarovits,C. Lindsey, J. Marquez, N. Minafra , M. Murray , M. Nickel, C. Rogan , C. Royon,R. Salvatico , S. Sanders, E. Schmitz, C. Smith , J.D. Tapia Takaki , Q. Wang , Z. Warner,J. Williams , G. Wilson

Kansas State University, Manhattan, Kansas, USAS. Duric, A. Ivanov , K. Kaadze , D. Kim, Y. Maravin , T. Mitchell, A. Modak, K. Nam,R. Taylor

Lawrence Livermore National Laboratory, Livermore, California, USAF. Rebassoo, D. Wright

University of Maryland, College Park, Maryland, USAE. Adams, A. Baden, O. Baron, A. Belloni , S.C. Eno , N.J. Hadley , S. Jabeen ,R.G. Kellogg, T. Koeth, A.C. Mignerey, S. Nabili, C. Palmer , M. Seidel , A. Skuja , L. Wang,K. Wong

Massachusetts Institute of Technology, Cambridge, Massachusetts, USAD. Abercrombie, G. Andreassi, R. Bi, S. Brandt, W. Busza , I.A. Cali, Y. Chen ,M. D’Alfonso , J. Eysermans, C. Freer , G. Gomez Ceballos, M. Goncharov, P. Harris, M. Hu,M. Klute , D. Kovalskyi , J. Krupa, Y.-J. Lee , C. Mironov , C. Paus , D. Rankin ,C. Roland , G. Roland, Z. Shi , G.S.F. Stephans , J. Wang, Z. Wang , B. Wyslouch

University of Minnesota, Minneapolis, Minnesota, USAR.M. Chatterjee, A. Evans , P. Hansen, J. Hiltbrand, Sh. Jain , M. Krohn, Y. Kubota,J. Mans , M. Revering, R. Rusack , R. Saradhy, N. Schroeder , N. Strobbe , M.A. Wadud

University of Mississippi, Oxford, Mississippi, USAJ.G. Acosta, L.M. Cremaldi , S. Oliveros , L. Perera , D. Summers†

University of Nebraska-Lincoln, Lincoln, Nebraska, USAE. Avdeeva, K. Bloom , M. Bryson, S. Chauhan , D.R. Claes, C. Fangmeier, L. Finco ,F. Golf , C. Joo, I. Kravchenko , F. Meier , M. Musich, I. Reed, J.E. Siado, G.R. Snow†,W. Tabb, F. Yan, A.G. Zecchinelli

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https://orcid.org/0000-0003-3681-0649

https://orcid.org/0000-0002-7427-1079

https://orcid.org/0000-0003-1770-5309

https://orcid.org/0000-0003-2840-1087

https://orcid.org/0000-0002-7633-749X

https://orcid.org/0000-0002-8336-6141

https://orcid.org/0000-0001-8835-8282

https://orcid.org/0000-0001-5550-7827

https://orcid.org/0000-0002-2570-064X

https://orcid.org/0000-0002-9002-4959

https://orcid.org/0000-0002-4272-8900

https://orcid.org/0000-0002-6544-5794

https://orcid.org/0000-0002-2630-5465

https://orcid.org/0000-0003-3567-9351

https://orcid.org/0000-0003-0068-0395

https://orcid.org/0000-0001-9748-5902

80

State University of New York at Buffalo, Buffalo, New York, USAG. Agarwal , H. Bandyopadhyay , L. Hay , I. Iashvili , A. Kharchilava, C. McLean ,D. Nguyen, J. Pekkanen , S. Rappoccio , A. Williams

Northeastern University, Boston, Massachusetts, USAG. Alverson , E. Barberis, Y. Haddad , A. Hortiangtham, J. Li , G. Madigan,B. Marzocchi , D.M. Morse , V. Nguyen, T. Orimoto , A. Parker, L. Skinnari ,A. Tishelman-Charny, T. Wamorkar, B. Wang , A. Wisecarver, D. Wood

Northwestern University, Evanston, Illinois, USAS. Bhattacharya , J. Bueghly, Z. Chen , A. Gilbert , T. Gunter , K.A. Hahn, Y. Liu,N. Odell, M.H. Schmitt , K. Sung, M. Velasco

University of Notre Dame, Notre Dame, Indiana, USAR. Band , R. Bucci, A. Das , N. Dev , R. Goldouzian , M. Hildreth, K. Hur-tado Anampa , C. Jessop , K. Lannon , J. Lawrence, N. Loukas , D. Lutton, N. Marinelli,I. Mcalister, T. McCauley , C. Mcgrady, K. Mohrman, Y. Musienko52, R. Ruchti, P. Siddireddy,A. Townsend, M. Wayne, A. Wightman, M. Zarucki , L. Zygala

The Ohio State University, Columbus, Ohio, USAB. Bylsma, B. Cardwell, L.S. Durkin , B. Francis , C. Hill , M. Nunez Ornelas , K. Wei,B.L. Winer, B.R. Yates

Princeton University, Princeton, New Jersey, USAF.M. Addesa , B. Bonham , P. Das , G. Dezoort, P. Elmer , A. Frankenthal ,B. Greenberg , N. Haubrich, S. Higginbotham, A. Kalogeropoulos , G. Kopp, S. Kwan ,D. Lange, D. Marlow , K. Mei , I. Ojalvo, J. Olsen , D. Stickland , C. Tully

University of Puerto Rico, Mayaguez, Puerto Rico, USAS. Malik , S. Norberg, J.E. Ramirez Vargas

Purdue University, West Lafayette, Indiana, USAA.S. Bakshi, V.E. Barnes , R. Chawla , S. Das , L. Gutay, M. Jones , A.W. Jung ,S. Karmarkar, D. Kondratyev , A.M. Koshy, M. Liu, G. Negro, N. Neumeister , G. Paspalaki,S. Piperov , A. Purohit, J.F. Schulte , M. Stojanovic19, J. Thieman , F. Wang , R. Xiao ,W. Xie

Purdue University Northwest, Hammond, Indiana, USAJ. Dolen , N. Parashar

Rice University, Houston, Texas, USAA. Baty , M. Decaro, S. Dildick , K.M. Ecklund , S. Freed, P. Gardner, F.J.M. Geurts ,A. Kumar , W. Li, H. Liu, T. Nussbaum, B.P. Padley , R. Redjimi, W. Shi ,A.G. Stahl Leiton , S. Yang , L. Zhang, Y. Zhang

University of Rochester, Rochester, New York, USAA. Bodek , P. de Barbaro, R. Demina , J.L. Dulemba , C. Fallon, T. Ferbel , M. Galanti,A. Garcia-Bellido , O. Hindrichs , A. Khukhunaishvili, E. Ranken, R. Taus

Rutgers, The State University of New Jersey, Piscataway, New Jersey, USAE. Bartz , B. Chiarito, J.P. Chou , A. Gandrakota , Y. Gershtein , E. Halkiadakis ,A. Hart, M. Heindl , O. Karacheban26 , I. Laflotte, A. Lath , R. Montalvo, K. Nash,M. Osherson, S. Salur , S. Schnetzer, S. Somalwar , R. Stone, S.A. Thayil , S. Thomas,H. Wang

https://orcid.org/0000-0002-2593-5297

https://orcid.org/0000-0001-9726-4915

https://orcid.org/0000-0002-7086-7641

https://orcid.org/0000-0003-1948-5901

https://orcid.org/0000-0002-7450-4805

https://orcid.org/0000-0002-6681-7668

https://orcid.org/0000-0002-5449-2560

https://orcid.org/0000-0003-4055-6532

https://orcid.org/0000-0001-6651-1178

https://orcid.org/0000-0003-4916-7752

https://orcid.org/0000-0001-5245-2074

https://orcid.org/0000-0001-6687-6214

https://orcid.org/0000-0003-3163-2169

https://orcid.org/0000-0002-8388-3341

https://orcid.org/0000-0002-2019-6755

https://orcid.org/0000-0003-0796-2475

https://orcid.org/0000-0002-6477-801X

https://orcid.org/0000-0002-0526-6161

https://orcid.org/0000-0003-4521-6086

https://orcid.org/0000-0001-7560-5790

https://orcid.org/0000-0002-7444-5622

https://orcid.org/0000-0003-0814-3578

https://orcid.org/0000-0003-4873-0523

https://orcid.org/0000-0001-9115-9698

https://orcid.org/0000-0003-2792-0491

https://orcid.org/0000-0002-0295-249X

https://orcid.org/0000-0002-9779-3566

https://orcid.org/0000-0002-6885-3611

https://orcid.org/0000-0002-9706-0098

https://orcid.org/0000-0003-0049-6918

https://orcid.org/0000-0001-6589-8286

https://orcid.org/0000-0003-1510-5772

https://orcid.org/0000-0002-0477-1051

https://orcid.org/0000-0002-1414-6583

https://orcid.org/0000-0003-0059-0779

https://orcid.org/0000-0003-2663-7379

https://orcid.org/0000-0001-7366-1318

https://orcid.org/0000-0003-0484-5804

https://orcid.org/0000-0002-2982-7621

https://orcid.org/0000-0002-9770-1377

https://orcid.org/0000-0001-6830-3356

https://orcid.org/0000-0002-2583-5982

https://orcid.org/0000-0002-4922-1934

https://orcid.org/0000-0003-3444-0314

https://orcid.org/0000-0002-5308-7707

https://orcid.org/0000-0002-6395-1079

https://orcid.org/0000-0003-2057-2025

https://orcid.org/0000-0002-9361-5762

https://orcid.org/0000-0003-4702-8820

https://orcid.org/0000-0001-6771-2174

https://orcid.org/0000-0002-6356-2655

https://orcid.org/0000-0002-4897-5857

https://orcid.org/0000-0001-6939-3445

https://orcid.org/0000-0003-4802-6819

https://orcid.org/0000-0001-6701-9265

https://orcid.org/0000-0002-9951-4583

https://orcid.org/0000-0003-3068-3212

https://orcid.org/0000-0002-7874-2480

https://orcid.org/0000-0003-2356-1700

https://orcid.org/0000-0002-9266-7819

https://orcid.org/0000-0003-4421-680X

https://orcid.org/0000-0001-7684-6588

https://orcid.org/0000-0002-8313-0809

https://orcid.org/0000-0001-7292-8527

https://orcid.org/0000-0003-1430-9191

https://orcid.org/0000-0003-1141-3823

https://orcid.org/0000-0001-5310-3466

https://orcid.org/0000-0003-0554-4755

https://orcid.org/0000-0002-6976-4637

https://orcid.org/0000-0003-2856-9090

https://orcid.org/0000-0002-5180-6595

https://orcid.org/0000-0002-3572-5701

https://orcid.org/0000-0002-8102-9002

https://orcid.org/0000-0002-5397-252X

https://orcid.org/0000-0002-2075-8631

https://orcid.org/0000-0002-6812-761X

https://orcid.org/0000-0003-0409-0341

https://orcid.org/0000-0002-7852-167X

https://orcid.org/0000-0002-9842-7015

https://orcid.org/0000-0002-6733-131X

https://orcid.org/0000-0002-1407-1972

https://orcid.org/0000-0001-7640-5264

https://orcid.org/0000-0001-8062-3192

https://orcid.org/0000-0001-6315-905X

https://orcid.org/0000-0003-4860-3233

https://orcid.org/0000-0002-4871-5449

https://orcid.org/0000-0002-3584-7856

https://orcid.org/0000-0002-2831-463X

https://orcid.org/0000-0002-2785-3762

https://orcid.org/0000-0003-0228-9760

https://orcid.org/0000-0002-4995-9285

https://orcid.org/0000-0002-8856-7401

https://orcid.org/0000-0002-1469-0335

https://orcid.org/0000-0002-3027-0752

81

University of Tennessee, Knoxville, Tennessee, USAH. Acharya, A.G. Delannoy , S. Fiorendi , S. Spanier

Texas A&M University, College Station, Texas, USAO. Bouhali98 , M. Dalchenko , A. Delgado , R. Eusebi, J. Gilmore, T. Huang, T. Kamon99,H. Kim , S. Luo , S. Malhotra, R. Mueller, D. Overton, D. Rathjens , A. Safonov

Texas Tech University, Lubbock, Texas, USAN. Akchurin, J. Damgov, V. Hegde, S. Kunori, K. Lamichhane, S.W. Lee , T. Mengke,S. Muthumuni , T. Peltola , I. Volobouev, Z. Wang, A. Whitbeck

Vanderbilt University, Nashville, Tennessee, USAE. Appelt , P. D’Angelo, S. Greene, A. Gurrola , W. Johns, A. Melo, H. Ni, K. Padeken ,F. Romeo , P. Sheldon , S. Tuo, J. Velkovska

University of Virginia, Charlottesville, Virginia, USAM.W. Arenton , B. Cox , G. Cummings , J. Hakala , R. Hirosky , M. Joyce ,A. Ledovskoy , A. Li, C. Neu , C.E. Perez Lara , B. Tannenwald , S. White , E. Wolfe

Wayne State University, Detroit, Michigan, USAN. Poudyal

University of Wisconsin - Madison, Madison, WI, Wisconsin, USAK. Black , T. Bose , C. Caillol, S. Dasu , I. De Bruyn , P. Everaerts , F. Fienga ,C. Galloni, H. He, M. Herndon , A. Herve, U. Hussain, A. Lanaro, A. Loeliger, R. Loveless,J. Madhusudanan Sreekala , A. Mallampalli, A. Mohammadi, D. Pinna, A. Savin, V. Shang,V. Sharma , W.H. Smith , D. Teague, S. Trembath-Reichert, W. Vetens

†: Deceased1: Also at TU Wien, Wien, Austria2: Also at Institute of Basic and Applied Sciences, Faculty of Engineer-ing, Arab Academy for Science, Technology and Maritime Transport, Alexandria, Egypt3: Also at Universite Libre de Bruxelles, Bruxelles, Belgium4: Also at Universidade Estadual de Campinas, Campinas, Brazil5: Also at Federal University of Rio Grande do Sul, Porto Alegre, Brazil6: Also at The University of the State of Amazonas, Manaus, Brazil7: Also at University of Chinese Academy of Sciences, Beijing, China8: Also at Department of Physics, Tsinghua University, Beijing, China9: Also at UFMS, Nova Andradina, Brazil10: Also at Nanjing Normal University Department of Physics, Nanjing, China11: Now at The University of Iowa, Iowa City, Iowa, USA12: Also at Deutsches Elektronen-Synchrotron, Hamburg, Germany13: Also at Institute for Theoretical and Experimental Physics named by A.I. Alikhanov of NRC ‘Kur-chatov Institute’, Moscow, Russia14: Also at Joint Institute for Nuclear Research, Dubna, Russia15: Also at Cairo University, Cairo, Egypt16: Also at Suez University, Suez, Egypt17: Now at British University in Egypt, Cairo, Egypt18: Also at CERN, European Organization for Nuclear Research, Geneva, Switzerland19: Also at Purdue University, West Lafayette, Indiana, USA20: Also at Universite de Haute Alsace, Mulhouse, France21: Also at Tbilisi State University, Tbilisi, Georgia22: Also at Erzincan Binali Yildirim University, Erzincan, Turkey

https://orcid.org/0000-0003-1252-6213

https://orcid.org/0000-0003-3273-9419

https://orcid.org/0000-0002-8438-3197

https://orcid.org/0000-0001-7139-7322

https://orcid.org/0000-0002-0137-136X

https://orcid.org/0000-0003-3453-7204

https://orcid.org/0000-0003-4986-1728

https://orcid.org/0000-0003-3122-4245

https://orcid.org/0000-0002-8420-1488

https://orcid.org/0000-0001-9497-5471

https://orcid.org/0000-0002-3388-8339

https://orcid.org/0000-0003-0432-6895

https://orcid.org/0000-0002-4732-4008

https://orcid.org/0000-0003-3389-4584

https://orcid.org/0000-0002-2793-4052

https://orcid.org/0000-0001-7251-9125

https://orcid.org/0000-0002-1297-6065

https://orcid.org/0000-0003-1550-5223

https://orcid.org/0000-0003-1423-5241

https://orcid.org/0000-0002-6188-1011

https://orcid.org/0000-0003-3752-4759

https://orcid.org/0000-0002-8045-7806

https://orcid.org/0000-0001-9586-3316

https://orcid.org/0000-0003-0304-6330

https://orcid.org/0000-0003-1112-5880

https://orcid.org/0000-0003-4861-0943

https://orcid.org/0000-0003-3644-8627

https://orcid.org/0000-0003-0199-8864

https://orcid.org/0000-0002-5570-8095

https://orcid.org/0000-0002-6181-4935

https://orcid.org/0000-0001-6553-4933

https://orcid.org/0000-0003-4278-3464

https://orcid.org/0000-0001-7320-5080

https://orcid.org/0000-0001-8026-5380

https://orcid.org/0000-0001-5993-9045

https://orcid.org/0000-0003-1704-4360

https://orcid.org/0000-0003-3848-324X

https://orcid.org/0000-0001-5978-4952

https://orcid.org/0000-0003-3043-1090

https://orcid.org/0000-0003-2590-763X

https://orcid.org/0000-0003-1287-1471

https://orcid.org/0000-0003-3195-0909

https://orcid.org/0000-0003-1058-1163

82

23: Also at RWTH Aachen University, III. Physikalisches Institut A, Aachen, Germany24: Also at University of Hamburg, Hamburg, Germany25: Also at Isfahan University of Technology, Isfahan, Iran26: Also at Brandenburg University of Technology, Cottbus, Germany27: Also at Forschungszentrum Julich, Juelich, Germany28: Also at Physics Department, Faculty of Science, Assiut University, Assiut, Egypt29: Also at Karoly Robert Campus, MATE Institute of Technology, Gyongyos, Hungary30: Also at Institute of Physics, University of Debrecen, Debrecen, Hungary31: Also at Institute of Nuclear Research ATOMKI, Debrecen, Hungary32: Also at MTA-ELTE Lendulet CMS Particle and Nuclear Physics Group, Eotvos Lorand Uni-versity, Budapest, Hungary33: Also at Wigner Research Centre for Physics, Budapest, Hungary34: Also at IIT Bhubaneswar, Bhubaneswar, India35: Also at Institute of Physics, Bhubaneswar, India36: Also at G.H.G. Khalsa College, Punjab, India37: Also at Shoolini University, Solan, India38: Also at University of Hyderabad, Hyderabad, India39: Also at University of Visva-Bharati, Santiniketan, India40: Also at Indian Institute of Technology (IIT), Mumbai, India41: Also at Sharif University of Technology, Tehran, Iran42: Also at Department of Physics, University of Science and Technology of Mazandaran,Behshahr, Iran43: Now at INFN Sezione di Bari, Universita di Bari, Politecnico di Bari, Bari, Italy44: Also at Italian National Agency for New Technologies, Energy and Sustainable Eco-nomic Development, Bologna, Italy45: Also at Centro Siciliano di Fisica Nucleare e di Struttura Della Materia, Catania, Italy46: Also at Horia Hulubei National Institute of Physics and Nuclear Engineering (IFIN-HH),Bucharest, Romania47: Also at Universita di Napoli ’Federico II’, Napoli, Italy48: Also at Consiglio Nazionale delle Ricerche - Istituto Officina dei Materiali, PERUGIA, Italy49: Also at Riga Technical University, Riga, Latvia50: Also at Consejo Nacional de Ciencia y Tecnologıa, Mexico City, Mexico51: Also at IRFU, CEA, Universite Paris-Saclay, Gif-sur-Yvette, France52: Also at Institute for Nuclear Research, Moscow, Russia53: Now at National Research Nuclear University ’Moscow Engineering Physics Insti-tute’ (MEPhI), Moscow, Russia54: Also at Institute of Nuclear Physics of the Uzbekistan Academy of Sciences, Tashkent,Uzbekistan55: Also at St. Petersburg Polytechnic University, St. Petersburg, Russia56: Also at University of Florida, Gainesville, Florida, USA57: Also at Imperial College, London, United Kingdom58: Also at P.N. Lebedev Physical Institute, Moscow, Russia59: Also at California Institute of Technology, Pasadena, California, USA60: Also at INFN Sezione di Padova, Universita di Padova, Padova, Italy, Univer-sita di Trento, Trento, Italy, Padova, Italy61: Also at Budker Institute of Nuclear Physics, Novosibirsk, Russia62: Also at Faculty of Physics, University of Belgrade, Belgrade, Serbia63: Also at Trincomalee Campus, Eastern University, Sri Lanka, Nilaveli, Sri Lanka64: Also at INFN Sezione di Pavia, Universita di Pavia, Pavia, Italy

83

65: Also at National and Kapodistrian University of Athens, Athens, Greece66: Also at Ecole Polytechnique Federale Lausanne, Lausanne, Switzerland67: Also at Universitat Zurich, Zurich, Switzerland68: Also at Stefan Meyer Institute for Subatomic Physics, Vienna, Austria69: Also at Laboratoire d’Annecy-le-Vieux de Physique des Particules, IN2P3-CNRS, Annecy-le-Vieux, France70: Also at Sırnak University, Sirnak, Turkey71: Also at Near East University, Research Center of Experimental Health Science, Nicosia,Turkey72: Also at Konya Technical University, Konya, Turkey73: Also at Piri Reis University, Istanbul, Turkey74: Also at Adiyaman University, Adiyaman, Turkey75: Also at Ozyegin University, Istanbul, Turkey76: Also at Izmir Institute of Technology, Izmir, Turkey77: Also at Necmettin Erbakan University, Konya, Turkey78: Also at Bozok Universitetesi Rektorlugu, Yozgat, Turkey79: Also at Marmara University, Istanbul, Turkey80: Also at Milli Savunma University, Istanbul, Turkey81: Also at Kafkas University, Kars, Turkey82: Also at Istanbul Bilgi University, Istanbul, Turkey83: Also at Hacettepe University, Ankara, Turkey84: Also at Istanbul University - Cerrahpasa, Faculty of Engineering, Istanbul, Turkey85: Also at Vrije Universiteit Brussel, Brussel, Belgium86: Also at School of Physics and Astronomy, University of Southampton, Southampton,United Kingdom87: Also at Rutherford Appleton Laboratory, Didcot, United Kingdom88: Also at IPPP Durham University, Durham, United Kingdom89: Also at Monash University, Faculty of Science, Clayton, Australia90: Also at Universita di Torino, Torino, Italy91: Also at Bethel University, St. Paul, Minneapolis, USA92: Also at Karamanoglu Mehmetbey University, Karaman, Turkey93: Also at Ain Shams University, Cairo, Egypt94: Also at Bingol University, Bingol, Turkey95: Also at Georgian Technical University, Tbilisi, Georgia96: Also at Sinop University, Sinop, Turkey97: Also at Erciyes University, Kayseri, Turkey98: Also at Texas A&M University at Qatar, Doha, Qatar99: Also at Kyungpook National University, Daegu, Korea

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Run 2 Tracker Alignment Performance