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Jet Performance in Run2 at CMS
Jet reconstruction at CMSp
Silicon tracker
Electromagnetic calorimeter
Hadron calorimeter
Particle-Flow algorithm
identifies and reconstructseach particle by combining the
information from all thesubdetectors
Jet Algorithm(Anti-kT)
Particles, CaloTowers,PF, Tracks
GenJets,CaloJets, PFJets
Giorgia Rauco (University of Zurich) 2 / 14 BOOST 2016
Jet Performance in Run2 at CMS
Jet reconstruction at CMSp
Silicon tracker
Electromagnetic calorimeter
Hadron calorimeter
Particle-Flow algorithm
identifies and reconstructseach particle by combining the
information from all thesubdetectors
Jet Algorithm(Anti-kT)
Particles, CaloTowers,PF, Tracks
GenJets,CaloJets, PFJets
Giorgia Rauco (University of Zurich) 2 / 14 BOOST 2016
Jet Performance in Run2 at CMS
Jet reconstruction at CMSp
Silicon tracker
Electromagnetic calorimeter
Hadron calorimeter
Particle-Flow algorithm
identifies and reconstructseach particle by combining the
information from all thesubdetectors
Jet Algorithm(Anti-kT)
Particles, CaloTowers,PF, Tracks
GenJets,CaloJets, PFJets
Giorgia Rauco (University of Zurich) 2 / 14 BOOST 2016
Jet Performance in Run2 at CMS
Jet reconstruction at CMSp
Silicon tracker
Electromagnetic calorimeter
Hadron calorimeter
Particle-Flow algorithm
identifies and reconstructseach particle by combining the
information from all thesubdetectors
Jet Algorithm(Anti-kT)
Particles, CaloTowers,PF, Tracks
GenJets,CaloJets, PFJets
Giorgia Rauco (University of Zurich) 2 / 14 BOOST 2016
Jet Performance in Run2 at CMS
Jet reconstruction at CMSp
Silicon tracker
Electromagnetic calorimeter
Hadron calorimeter
Particle-Flow algorithm
identifies and reconstructseach particle by combining the
information from all thesubdetectors
Jet Algorithm(Anti-kT)
Particles, CaloTowers,PF, Tracks
GenJets,CaloJets, PFJets
Giorgia Rauco (University of Zurich) 2 / 14 BOOST 2016
Jet Performance in Run2 at CMS
Jet energy correctionsp
arXiv:1607.03663, CMS DP-2016/020
correct reconstructed jets back to particle level
L1(Pile-Up)Reconstructed jet
L2L3(pT ,η)
L2L3Residuals
(pT ,η)Calibrated jet
factorized approach:
1 L1: correction for average offset-energy from pile-up
2 L2L3: correction for (η,pT ) dependence of jet response
3 L2L3Residuals: correction for residual data-simulation differences
• applied on data only
New in Run2: PileUp Per Particle Identification (PUPPI), which assigning weights to PFparticle depending on the weighted pT sum of the particles surrounding it
Giorgia Rauco (University of Zurich) 3 / 14 BOOST 2016
Jet Performance in Run2 at CMS
Pile-up offset correctionspp
L1
(Pile-Up)Reconstructed jet
L2L3(pT ,η)
L2L3Residuals
(pT ,η)Calibrated jet
100mm10mm,0.2
• hard jets from overlapping low-pT PU jets
real jet pile-up jet
• overall increase of the jet energy• corrected with hybrid jet area method
• data/simulation scale factors areextracted
• from Zero Bias data• with the Random Cone methodORC 〈η,〈ρ〉〉= 〈pT ,cone〉 [η,µ]
Zero Bias event
Giorgia Rauco (University of Zurich) 4 / 14 BOOST 2016
Jet Performance in Run2 at CMS
Simulated response correctionspp
L1(Pile-Up)Reconstructed jet
L2L3
(pT ,η)
L2L3Residuals
(pT ,η)Calibrated jet
100mm10mm,0.2MC truth corrections derived from QCD dijet events
• jet pT misreconstruted
• reco-level and gen-level jets are matched
• response defined in bin of pT ,ptcl and η as:
R(〈pT ,reco 〉 ,η) =〈pT ,reco 〉〈pT ,ptcl 〉
[pT ,ptcl ,η]
gen-jet
reco-jet
• stable response in barrelobtained
• stronger pT-dependence inendcaps and HF
• overall improvements from Run1in HF calibration thanks to HFdetector upgrade
Giorgia Rauco (University of Zurich) 5 / 14 BOOST 2016
Jet Performance in Run2 at CMS
Residual corrections with datapp
L1(Pile-Up)Reconstructed jet
L2L3(pT ,η)
L2L3
Residuals
(pT ,η)Calibrated jet
100mm10mm,0.2residuals data/MC scale factors for the dependency of the jet response:
100mm10mm,0.2
1. relative η-dependent corrections
• back-to-back dijets events(tag jet in the barrel, probe jet free to scan
the whole detector)
• response of probe jet relative totag jet(calibrate the response of a jet at a given η
to the one for jets in |η|<1.3)
good description of relative responsescale within tracker coverage
Giorgia Rauco (University of Zurich) 6 / 14 BOOST 2016
Jet Performance in Run2 at CMS
Residual corrections with datapp
L1(Pile-Up)Reconstructed jet
L2L3(pT ,η)
L2L3
Residuals
(pT ,η)Calibrated jet
100mm10mm,0.2residuals data/MC scale factors for the dependency of the jet response:
100mm10mm,0.2
2. absolute pT-dependent corrections
• pT -balance and ~/ET Projection
Fraction methods used
• a constant correction is derivedusing Z(µµ) + jet events
• response from multiple channels(γ/Z+jet and multijets) arecombined in a global fit
Giorgia Rauco (University of Zurich) 6 / 14 BOOST 2016
Jet Performance in Run2 at CMS
Residual corrections with datapp
L1(Pile-Up)Reconstructed jet
L2L3(pT ,η)
L2L3
Residuals
(pT ,η)Calibrated jet
100mm10mm,0.2residuals data/MC scale factors for the dependency of the jet response:
100mm10mm,0.2
2. absolute pT-dependent corrections
• pT -balance and ~/ET Projection
Fraction methods used
• a constant correction is derivedusing Z(µµ) + jet events
• response from multiple channels(γ/Z+jet and multijets) arecombined in a global fit
pT -balance
γ,Z events: jet fully captures parton thatbalances reference object
Rabs =pjetT
pγ,ZT
dijet events: tag (central jet) and probeapproach
Rrel =2+〈B〉2−〈B〉
where B =pprobeT −ptagT
2
Giorgia Rauco (University of Zurich) 6 / 14 BOOST 2016
Jet Performance in Run2 at CMS
Residual corrections with datapp
L1(Pile-Up)Reconstructed jet
L2L3(pT ,η)
L2L3
Residuals
(pT ,η)Calibrated jet
100mm10mm,0.2residuals data/MC scale factors for the dependency of the jet response:
100mm10mm,0.2
2. absolute pT-dependent corrections
• pT -balance and ~/ET Projection
Fraction methods used
• a constant correction is derivedusing Z(µµ) + jet events
• response from multiple channels(γ/Z+jet and multijets) arecombined in a global fit
MPF
~/ET solely due to jet mismeasurements
~pTγ,Z + ~pT
recoil = 0Rγ,Z ~pT
γ,Z +Rrecoil ~pTrecoil = −~/ET
Rrecoil = Rγ,Z +~/ET· ~pT
γ,Z
( ~pTγ,Z )2
≡ RMPF
Giorgia Rauco (University of Zurich) 6 / 14 BOOST 2016
Jet Performance in Run2 at CMS
Residual corrections with datapp
L1(Pile-Up)Reconstructed jet
L2L3(pT ,η)
L2L3
Residuals
(pT ,η)Calibrated jet
100mm10mm,0.2residuals data/MC scale factors for the dependency of the jet response:
100mm10mm,0.2
2. absolute pT-dependent corrections
• pT -balance and ~/ET Projection
Fraction methods used
• a constant correction is derivedusing Z(µµ) + jet events
• response from multiple channels(γ/Z+jet and multijets) arecombined in a global fit
jet response ∼0.98 at low pT and∼1 at higher pT
Giorgia Rauco (University of Zurich) 6 / 14 BOOST 2016
Jet Performance in Run2 at CMS
Jet energy correction Uncertaintiesp
• classified in four broadcategories
1 pileup offset2 η-relative calibration of jet
energy scale3 pT -relative calibration of jet
energy scale:
most important at high pT4 jet flavour response5 (time dependence)
• Pileup uncertainty dominantbelow 50 GeV
• Other important uncertainties:1 absolute scale within |η| <32 relative scale at |η| >3
Giorgia Rauco (University of Zurich) 7 / 14 BOOST 2016
Jet Performance in Run2 at CMS
Jet energy resolutionp
jet pT resolution:
width of response distributionJER = σ
(pT ,recopT ,ptcl
)extracted from MC simulation:
pT -asymmetry in dijet and pT -balancing in γ/Z + jets
resolution stable against pileup above jet pT =100 GeV andbetter than 10% (5%) resolution above 100 GeV (1 TeV)
Giorgia Rauco (University of Zurich) 8 / 14 BOOST 2016
Jet Performance in Run2 at CMS
Missing Transverse Energyp
CMS-JME-13-003, CMS DP-2016/017
Missing Transverse Energy (MET):
1 measure of momentum imbalance in the transverse plane
2 used to detect particles who don’t leave a signal in the detector (i.e. neutrinos)
3 crucial role in many physical analysis (i.e. SUSY and DM searches)
4 ~/ET = −∑~pT, sum over all observed final-state particles
(→ equal to the total ~pT of unobserved particles)
Giorgia Rauco (University of Zurich) 9 / 14 BOOST 2016
Jet Performance in Run2 at CMS
Scale and resolution measurementsp
Z /γ + jets events exploited:
• no genuine ~/ET is contained
• ~pT-balance between the vector boson and thehadronic system
• vector boson used as reference→measurescale and resolution of ~/ET
q̄ ′
q l−
l+
γ/Z
Z → e+e− Z → µ+µ− γ + jets
Giorgia Rauco (University of Zurich) 10 / 14 BOOST 2016
Jet Performance in Run2 at CMS
Resolutionp
~/ET resolution:
• geometrical approach used
• parametrization of the paralelland perpendicular recoils
~pT(γ/Z)
~/ET
~uT
u⊥
u||
parallel recoil perpendicolar recoil
• the resolutionincreases withincreasing pT
• the data and thesimulation curvesare in reasonableagreement foreach channel
Giorgia Rauco (University of Zurich) 11 / 14 BOOST 2016
Jet Performance in Run2 at CMS
Resolution with PUPP~/ETp
see Satoshi Hasegawa’s talk @BOOST2015 and CMS-DP-2015-034
PUPP~/ET: missing transverse energy (MET) determination using inputparticles from PUPPI algorithm
PUPPI provides better resolution, and it is stable with respect to pileup
Giorgia Rauco (University of Zurich) 12 / 14 BOOST 2016
Jet Performance in Run2 at CMS
Responsep
• ~/ET response is defined as themean of the parallel recoilcomponent of the recoil over theboson pT , −〈u||〉 /qT
• agreement between data andsimulation is reasonable for eachchannel
• ~/ET is fully able to recover thehadronic recoil activity from∼ 40 GeV
Giorgia Rauco (University of Zurich) 13 / 14 BOOST 2016
Jet Performance in Run2 at CMS
Conclusionsp
• latest jet energy scale and resolution measurement have beenpresented
• pile-up offset corrections from QCD dijet simulation• simulated jet response corrections extracted in function of η and T
determined from simulation• residual differences between data and simulation have been taken into
account too
• ~/ET scale and resolution have been measured• data agree with the expectations from the simulation
we can get the same performance with 25ns LHC running thanwe had with 50ns in Run 1
Giorgia Rauco (University of Zurich) 14 / 14 BOOST 2016
Jet Performance in Run2 at CMS
Conclusionsp
• latest jet energy scale and resolution measurement have beenpresented
• pile-up offset corrections from QCD dijet simulation• simulated jet response corrections extracted in function of η and T
determined from simulation• residual differences between data and simulation have been taken into
account too
• ~/ET scale and resolution have been measured• data agree with the expectations from the simulation
we can get the same performance with 25ns LHC running thanwe had with 50ns in Run 1
Giorgia Rauco (University of Zurich) 14 / 14 BOOST 2016
Jet Performance in Run2 at CMS
Questions?p
• Why is there a step in the absolutepT -dependent JEC?
• → Still under investigation. Most likelydue to HCAL noise rejectionpT -dependence for 25 ns out-of-timepile-up rejection.
• Why is there a bump in the JECuncertainties at about 150 GeV?
• → See previous answer.
• What about jet mass scale and jet massresolution?
• → Not approved in time. Hopefully forICHEP.
• Systematics?• →Work in progress.
Giorgia Rauco (University of Zurich) 15 / 14 BOOST 2016
Jet Performance in Run2 at CMS
ADDITIONAL MATERIAL
Giorgia Rauco (University of Zurich) 16 / 14 BOOST 2016
Jet Performance in Run2 at CMS
Random-Cone Methodp
the offset data/simulation scale factor is estimated from Zero Bias (ZB) dataand simulation using the Random Cone method
ZB has no energy deposition from hard interactions, so the averagetransverse momentum 〈pT ,cone〉(η) of particles in a randomly placed cone
centered at (η,φ) can be identified with the average offset due to pileup,ORC (η):
O(η,〈ρ〉) = 〈pT ,cone 〉 [η,µ]
For deriving the offset scale factor, the Random Cone measurement is fittedwith a quadratic function of ρ:
ORC = p0ρp1 + ρ2p2
finally, the offset scale factor is defined as:
SF =OdataRC (η,〈ρ〉)OMCRC (η,〈ρ〉)
Giorgia Rauco (University of Zurich) 17 / 14 BOOST 2016
Jet Performance in Run2 at CMS
Hybrid Jet Area Methodp
it uses the effective area of the jets multiplied by the average energy densityin the event to calculate the offset energy to be subtracted from the jets
Tevatron Method
• average pT in a jet cone due topile-up
• η-dependent average offset O(η)correction versus NPV
Coffset (NPV ,η,Eraw ) = 1− (NPV −1)OE (η)Eraw
Jet Area Method
• average pT density per unit jetarea
• it only uses ρ and Aj• η-independent
Coffset (ρ,Aj ,pT ,raw ) = 1−(ρ−ρUE )·Aj
pT ,raw
the Hybrid Jet Area Method imports η-dependence from Tevatron methodinto Jet Area Method
Chybrid (pT ,uncorr ,η,Aj ,ρ) = 1− [ρ0(η)+ρ·βη·(1+γ(η)·log(pT ,uncorr))]·AjpT ,uncorr
Giorgia Rauco (University of Zurich) 18 / 14 BOOST 2016
Jet Performance in Run2 at CMS
pT balancing and asymmetry methodsp
pT balancing
(for γ/Z events)
σ
pjet ,recoTpγ,recoT
= σ
pjet ,recoTpjet ,ptclT
⊕ σ p
jet ,ptclTpγ,partT
⊕ σ pγ,partT
pγ,recoT
σB = σpT
pT⊕ σUE+OOC+ISR+FSR ⊕ σgamma
after extrapolating the secondary jet activityto zero the effects of ISR and FSR become
negligible
σJER = σB · krad σPLI σγ
pT asymmetry
(for dijets events)
A =p1T−p
2T
p1T+p2
T
and its resolution is defined as
σA ·krad =σJER ,probe
2 ⊕ σJER ,tag2 ⊕σPLI ,dijet
and if both jets are in the same regionand share the same JER
σJER =√
2(σA · krad σPLI ,dijet )
Giorgia Rauco (University of Zurich) 19 / 14 BOOST 2016
Jet Performance in Run2 at CMS
pT balancing and MPF methodsp
pT -balance
jet fully captures parton that balancesreference object
RpT = 1−〈A〉1+〈A〉
where
A=pprobeT −ptagT
2paveT
MPF
~/ET solely due to jet mismeasurements
RMPF = 1−〈B〉1+〈B〉
where
B = 1 +~ET ,miss · ~pT
tag
2paveT ·| ~pTtag |
Giorgia Rauco (University of Zurich) 20 / 14 BOOST 2016
Jet Performance in Run2 at CMS
Jet energy resolution in forward regionp
Giorgia Rauco (University of Zurich) 21 / 14 BOOST 2016
Jet Performance in Run2 at CMS
Jet energy resolution data/MC scale factorsp
Resolution Data/MCscale factors of1.1-1.2 except for theEC-HF transitionregion around η=3
Giorgia Rauco (University of Zurich) 22 / 14 BOOST 2016
Jet Performance in Run2 at CMS
JEC with 2015 datap
Giorgia Rauco (University of Zurich) 23 / 14 BOOST 2016
Jet Performance in Run2 at CMS
Better HF calibrationp
thanks to the new HF detector had been upgraded (2013-2014)
|ηJet |0 0.5 1 1.5 2 2.5 3 3.5 4 4.5
Sim
ulat
ed je
t res
pons
e
0.5
0.6
0.7
0.8
0.9
1
1.1
1.2
(8 TeV)CMS
= 10 GeVT
p = 30 GeV
Tp
= 100 GeVT
p
= 400 GeVT
p = 2000 GeV
Tp
R = 0.5, PF+CHST2012 JES: Anti-k
Barrel Endcap ForwardBB EC1 EC2 HF
pT -dependence of HF is better calibrated
Giorgia Rauco (University of Zurich) 24 / 14 BOOST 2016
Jet Performance in Run2 at CMS
Jet energy correction uncertaintyp
• Pileup uncertaintydominant below 50 GeV
• absolute scale within|η| <3
• relative scale at |η| >3
• Minimum uncertainty of∼ 0.7% at pT = 300 GeVand |η| <3
Giorgia Rauco (University of Zurich) 25 / 14 BOOST 2016
Jet Performance in Run2 at CMS
JEC uncertainties in Run1p
(GeV)T
p20 100 200 1000
JEC
unc
erta
inty
(%
)
0
1
2
3
4
5
6 (8 TeV)-119.7 fb
CMS Total uncertaintyExcl. flavor, timeAbsolute scaleRelative scale
=20)⟩µ⟨Pileup (Jet flavor (QCD)Time stability
R=0.5 PF+CHS| = 0
jetη|
(8 TeV)-119.7 fb
CMS
jetη
4− 2− 0 2 4JE
C u
ncer
tain
ty (
%)
0
1
2
3
4
5
6 (8 TeV)-119.7 fb
CMS Total uncertaintyExcl. flavor, timeAbsolute scaleRelative scale
=20)⟩µ⟨Pileup (Jet flavor (QCD)Time stability
R=0.5 PF+CHS = 30 GeV
Tp
(8 TeV)-119.7 fb
CMS
(GeV)T
p20 100 200 1000
JEC
unc
erta
inty
(%
)
0
1
2
3
4
5
6 (8 TeV)-119.7 fb
CMS Total uncertaintyExcl. flavor, timeAbsolute scaleRelative scale
=20)⟩µ⟨Pileup (Jet flavor (QCD)Time stability
R=0.5 PF+CHS| = 2.7
jetη|
(8 TeV)-119.7 fb
CMS
jetη
4− 2− 0 2 4
JEC
unc
erta
inty
(%
)
0
1
2
3
4
5
6 (8 TeV)-119.7 fb
CMS Total uncertaintyExcl. flavor, timeAbsolute scaleRelative scale
=20)⟩µ⟨Pileup (Jet flavor (QCD)Time stability
R=0.5 PF+CHS = 100 GeV
Tp
(8 TeV)-119.7 fb
CMS
(GeV)T
p20 100 200 1000
JEC
unc
erta
inty
(%
)
0
1
2
3
4
5
6 (8 TeV)-119.7 fb
CMS Total uncertaintyExcl. flavor, timeAbsolute scaleRelative scale
=20)⟩µ⟨Pileup (Jet flavor (QCD)Time stability
R=0.5 PF+CHS| = 3.5
jetη|
(8 TeV)-119.7 fb
CMS
jetη
4− 2− 0 2 4JE
C u
ncer
tain
ty (
%)
0
1
2
3
4
5
6 (8 TeV)-119.7 fb
CMS Total uncertaintyExcl. flavor, timeAbsolute scaleRelative scale
=20)⟩µ⟨Pileup (Jet flavor (QCD)Time stability
R=0.5 PF+CHS = 1000 GeV
Tp
(8 TeV)-119.7 fb
CMS
Giorgia Rauco (University of Zurich) 26 / 14 BOOST 2016
Jet Performance in Run2 at CMS
JER in Run1p
(GeV)T, ptcl
p20 30 100 200 1000
JER
00.05
0.10.150.2
0.250.3
0.350.4
0.450.5 (8 TeV)CMS Simulation
, R=0.5 (PF+CHS)TAnti-k|<1.3η|=0µ
< 10µ ≤0 < 20µ ≤10 < 30µ ≤20 < 40µ ≤30
, R=0.5 (PF+CHS)TAnti-k|<1.3η|=0µ
< 10µ ≤0 < 20µ ≤10 < 30µ ≤20 < 40µ ≤30
(GeV)T, ptcl
p20 30 100 200 1000
JER
00.05
0.10.150.2
0.250.3
0.350.4
0.450.5 (8 TeV)CMS Simulation
, R=0.7 (PF+CHS)TAnti-k|<1.3η|=0µ
< 10µ ≤0 < 20µ ≤10 < 30µ ≤20 < 40µ ≤30
, R=0.7 (PF+CHS)TAnti-k|<1.3η|=0µ
< 10µ ≤0 < 20µ ≤10 < 30µ ≤20 < 40µ ≤30
Giorgia Rauco (University of Zurich) 27 / 14 BOOST 2016
Jet Performance in Run2 at CMS
JEC in Run1p
-5 -4 -3 -2 -1 0 1 2 3 4 5
(G
eV)
⟩µ⟨ / ⟩
T,of
fset
p⟨
0.2
0.4
0.6
0.8
1PhotonsEM depositsNeutral hadronsHadronic depositsUnassoc. charged hadronsCharged hadrons
PhotonsEM depositsNeutral hadronsHadronic depositsUnassoc. charged hadronsCharged hadrons
R=0.5TAnti-k
> = 19µ<
Markers: Data, Histograms: MC
(8 TeV)-119.7 fb
CMS
η -5 -4 -3 -2 -1 0 1 2 3 4 5
Dat
a/M
C
1
1.2PFPF+CHSPFPF+CHS
|η|0 1 2 3 4 5
Rel
ativ
e co
rrec
tion
0.98
1
1.02
1.04
1.06
1.08
1.1
1.12
1.14
1.16
1.18
(jet)T
p
60 GeV
120 GeV
240 GeV
480 GeV
(8 TeV)-119.7 fb
CMS R=0.5 PF+CHSTAnti-k
(GeV)T
p40 100 200 1000
Pos
t-fit
jet r
espo
nse
(rat
io)
0.94
0.96
0.98
1
1.02
1.04
1.06
1.08 (8 TeV)-119.7 fb
CMS
balT
p
MPFMultijet+jetγ
ee)+jet→Z()+jetµµ→Z(
JES unc.
| < 1.3η| 0→ < 0.3 α
After global fit = 107.5 / 92dof / N2χ
Giorgia Rauco (University of Zurich) 28 / 14 BOOST 2016
Jet Performance in Run2 at CMS
PF jet compositionp
(GeV)T
p40 100 200 1000 2000
PF
ene
rgy
frac
tion
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
(8 TeV)-119.7 fbCMS
Charged pileupCharged hadronsPhotonsNeutral hadronsLeptons
| < 1.3η| R=0.5TAnti-k
Markers: DataHistogram: MC
(GeV)T
p40 100 200 1000 2000
Dat
a-M
C (
%)
2−
0
2 η5− 4− 3− 2− 1− 0 1 2 3 4 5
PF
ene
rgy
frac
tion
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
(8 TeV)-119.7 fbCMS
Charged pileupCharged hadronsPhotonsNeutral hadronsLeptonsForward hadronsForward photons
< 74 GeVT
56 < p R=0.5TAnti-k
Markers: DataHistogram: MC
η5− 4− 3− 2− 1− 0 1 2 3 4 5
Dat
a-M
C (
%)
4−
2−
0
2
4
Giorgia Rauco (University of Zurich) 29 / 14 BOOST 2016
Jet Performance in Run2 at CMS
Anomalous ~/ETp
~/ET distributions for eventspassing the dijet selection
without cleaning algorithmsapplied (open markers), withcleaning algorithms applied
(filled markers), and simulatedevents (filled histograms).
Giorgia Rauco (University of Zurich) 30 / 14 BOOST 2016
Jet Performance in Run2 at CMS
MET geometrical approachp
~pT(γ/Z)
~/ET
~uT
u⊥
u|| - ~pT: Z/γ transverse momentum- ~uT: hadronic recoil
~pT + ~uT + ~/ET = 0
parallel component perpendicular component
Giorgia Rauco (University of Zurich) 31 / 14 BOOST 2016
Jet Performance in Run2 at CMS
PUPP~/ETp
Good agreement between data and MC was seen in both.The stable performance of PUPPI was confirmed with data.
Giorgia Rauco (University of Zurich) 32 / 14 BOOST 2016
Jet Performance in Run2 at CMS
How puppi worksp
from Satoshi Hasegawa’s talk @BOOST2015
Giorgia Rauco (University of Zurich) 33 / 14 BOOST 2016