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CMS Search for hgγγ
Yuri Gershtein
…or… squeezing blood from stone
Yuri Gershtein
Hgγγ Search
! How come this is hard?!
4/6/12 Yuri Gershtein 3
M = 2E1E2 (1! cos!)
! Main idea: measure energy of the two photons and their opening angle
The Challenge ! Huge “irreducible” background from QCD di-photon
production (plus instrumental backgrounds)
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Tracker Material
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! Reasonably well described by simulation ! Degrades energy resolution
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What Material Does to Photons
! Generate back-to-back photons and scan event displays
Black: hits in tracker
Green: ECAL outline
Red: ET of ECAL cells
Several bad effects at once
• hard to identify (showers not narrow in azimuth)
• energy resolution has tails due to not clustered energy
• some energy never reaches ECAL (B = 4 Tesla!)
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10-4
10-3
10-2
10-1
0 5 10 15 20 25 30 35Reconstructed Et
Arb
itrar
y Un
its
Single electrons 30 GeV pt
Putting Humpty Dumpty Together Again
! Some of the spray may be recovered by making clusters wide in azimuth ! at a cost of picking up energy from underlying
event
! How to make optimal correction given the detector region and all reconstructed pieces of the photon?
Super-Cluster window
The First Squeeze ! Improve photon energy resolution ! Energy resolution is affected by
! Impact point in the calorimeter (containment) ! Whether the photon converted ! Radius of conversion ! The amount of material that electrons from conversion have to
traverse before impacting calorimeter
! Very non-trivial correlations between measured photon properties and the corrections that one needs to apply
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The First Squeeze ! Improve photon energy resolution ! Energy resolution is affected by
! Impact point in the calorimeter (containment) ! Whether the photon converted ! Radius of conversion ! The amount of material that electrons from conversion have to
traverse before impacting calorimeter
! Very non-trivial correlations between measured photon properties and the corrections that one needs to apply
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“trivial” in one dimension
Multivatiate Analyses ! Classification
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! Regression
! The math that goes into either is quite similar – multi-dimensional minimization problem
Boosted Decision Trees ! BDT’s have been used for classification problems in high
energy physics for more then a decade ! i.e. miniBOONE particle identification
! Robust against noisy variables ! Robust against outlier events ! Robust against discrete variables ! Fairly intuitive
! Boosting ! Instead of one tree use a weighted
ensemble of many trees
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root node
2.1
0.35 0.63 0.17
1.5
regression tree
The First Squeeze ! Improve photon energy resolution ! Energy resolution is affected by
! Impact point in the calorimeter (containment) ! Whether the photon converted ! Radius of conversion ! The amount of material that electrons from conversion have to
traverse before impacting calorimeter
! Depending on the region in the detector, can achieve up to 20% improvement in resolution compared to “standard” factorized correction method ! And can check that the used variables and their correlations are
described by simulation by comparing electrons from Z decays in data and MC
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The Second Squeeze ! Some photons are measured well, some are not – instead of
mixing all events together, can we separate them into classes and combine the searches in individual classes?
! First 5/fb result – just 2 variables: Barrel – Endcap and converted-unconverted based on shower narrowness (fraction of cluster energy in the 3x3 crystal matrix)
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The Second Squeeze ! Some photons are measured well, some are not – instead of
mixing all events together, can we separate them into classes and combine the searches in individual classes?
! First 5/fb result – just 2 variables: Barrel – Endcap and converted-unconverted based on shower narrowness (fraction of cluster energy in the 3x3 crystal matrix)
! Can we be smarter?
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The Second Squeeze ! Some photons are measured well, some are not – instead of
mixing all events together, can we separate them into classes and combine the searches in individual classes?
! First 5/fb result – just 2 variables: Barrel – Endcap and converted-unconverted based on shower narrowness (fraction of cluster energy in the 3x3 crystal matrix)
! Train the second regression – this time do not try to improve the resolution by asking BDT to guess the energy, ask the BDT to guess how well this particular photon is measured ! Same variables as for energy regression ! Different target – instead of Etrue/Ereco regress to |(Ereco-Etrue)/Etrue|
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Regression to resolution ! Regress to |Ereco-Etrue|/Etrue
! average value of that is 0.7985 of gaussian sigma
16 4/6/12 Yuri Gershtein
Fit resolution in bins of BDT
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simulation
Fit resolution in bins of BDTG
18
Just as with energy regression, we can check the resolution regression using observed width of Zgee
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simulation
Hgγγ resolution classification
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M = 2E1E2 (1! cos!)
! Mass resolution depends on the energy resolution of the photons and on the precision of the opening angle measurement
! If the vertex is reconstructed correctly (within ~1cm) the contribution of angle in mass resolution is negligible
! If not, angular resolution dominates
Choosing the right vertex
! Track activity in the best events – with no photon conversion – look very similar to a minimum bias event
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The Third Squeeze ! Using BDT to pick the correct vertex ! Simulation result on the Higgs signal sample
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no conversions with conversions
The Third Squeeze ! Total efficiency to get the right vertex ~80%
! Validated with Zgµµ and γ+jet events in data
! Higgs pT is a good predictor of whether the vertex is correct
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The Fourth Squeeze ! Instead of using one variable to predict the probability that
the vertex is picked correctly, use an MVA (BDT) ! pT of the di-photon system ! number of vertices ! per-vertex BDT values for the best three vertices ! Δz between first and second and third vertices ! “pulls” of the reconstructed conversions
4/6/12 Yuri Gershtein 23
z vtx!zconv! conv
Zgµµ
! In the hypothesis that the vertex is unknown, the resolution can be calculated analytically
as a function of photon’s η, φ, and the distance from (0,0,0) to ECAL cluster r
! This formula simplifies if r1=r2
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vertex impact on mass resolution
The story so far ! Minimized mass resolution
! regression to energy ! categorization of the vertices
! Evaluated mass resolution precision ! regression to energy resolution ! regression to vertex selection probability ! analytical formula for resolution due to incorrect vertex
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The story so far ! Minimized mass resolution
! regression to energy ! categorization of the vertices
! Evaluated mass resolution precision ! regression to energy resolution ! regression to vertex selection probability ! analytical formula for resolution due to incorrect vertex
! Left to do: ! Suppress instrumental backgrounds (photon ID MVA) ! Identify differences in kinematical features of signal and
background and make a final discriminant optimizing signal-to-background ratio (per-event categorization MVA)
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Final Classification ! Demonstration of what final classifier is sensitive to:
! separate Higgs MC events into high/low di-photon pT
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! Vertical lines correspond to optimized classifier bins ! Mass distributions are fit in classifier bins
! Colored histograms are event classes as in non-MVA analysis ! Note overlaps between the old categories!
high S/B low S/B high S/B low S/B
Higgs simulation
Higgs simulation
Final Classification ! Demonstration of what final classifier is sensitive to:
! plot mass resolution for each classifier bin
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Crunching the Numbers ! Five exclusive event categories
! VBF tagged events ! pT
γ > mγγ/2.18, mγγ/4, |η|<1.44 or 1.57<|η|<2.5 ! 2 jets, ET>30, 20 GeV, |η|<4.7 ! Δη> 3.5, mJJ>350 GeV, |Z|<2.5, Δφ(jj,γγ)>2.6
! Four BDT categories ! pT
γ > mγγ/3, mγγ/4, |η|<1.44 or 1.57<|η|<2.5 ! VBF tagged events are removed
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Crunching the Numbers
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! New analysis methods result in effectively almost 40% larger data sample
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Expected sensitivity@125 GeV is ~1.2σSM (compared to ~1.6σSM for ATLAS)
The result
The result
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2.9σ maximum local significance. After LLE in 110-150 GeV window the significance is 1.6σ (non-MVA numbers were 3.1σ and 1.8σ)
Cat 0-3 went from ~1.3×10-2 to ~3.0×10-2
Fermiophobic interpretation
A little different analysis ! VBF tag is essentially the
same (except no cut on classification MVA)
! add lepton tag from VH ! for events not tagged by
the two analyses above do a 2-D unbinned likelihood fit (mass vs higgs pT) in “old” categories
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Fermiophobic interpretation
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FP interpretation: with WW/ZZ
“van
illa”
FP
is n
ot f
avor
ed
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Challenge for 2012 ! Preliminary estimates on MC
! As-is analysis degrades by ~20%
! Pile-up ! Degrades ID ! Degrades energy resolution ! Increases chance of misidentifying the primary vertex ! Degrades VBF tag
! Particle flow techniques seem to help quite a bit ! Possible to recover and may be even improve
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Summary ! New MVA methods have been developed for Higgs
search in di-photons and give a substantial improvement in the expected statistical precision
! The fundamental conclusion of the re-analysis of the 2011 data is however unchanged
Need more data
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Summary ! New MVA methods have been developed for Higgs
search in di-photons and give a substantial improvement in the expected statistical precision
! The fundamental conclusion of the re-analysis of the 2011 data is however unchanged
Need more data
(but less then we’d needed without MVA’s)
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Systematic errors
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Boosted Decision Trees ! A toy example
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z = {1+ ay, x < 0!1+ by, x > 0
Boosted Decision Trees ! Ensemble of 1000 trees
! Tree parameters: depth=1, number of splits/variable = 4
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Boosted Decision Trees ! Ensemble of 1000 trees
! Tree parameters: depth=2, number of splits/variable = 4
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Boosted Decision Trees ! Ensemble of 1000 trees
! Tree parameters: depth=3, number of splits/variable = 4
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Boosted Decision Trees ! Ensemble of 1000 trees
! Tree parameters: depth=1, number of splits/variable = 100
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The Third Squeeze ! Use BDT to choose the “signal” vertex ! For all events
! “intensity” of the vertex
! tracks should follow direction of higgs recoil
! track sum should be similar to higgs recoil
! For events with at least one reconstructed conversion
! “pull”
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!pT!2
!pT! "!pT!!
!pT!!
!pT! "!pT!!
!pT! +!pT!!
z vtx!zconv! conv
vertex impact on mass resolution ! Toy MC: take ideal energy resolution, and *always* pick a
wrong vertex ! The error is on 1-cosα, so back-to-back configuration is less affected
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vertex impact on mass resolution ! Resolution also depends on the polar angles of the photons
4/6/12 Yuri Gershtein 48
more affected less affected
Instrumental Backgrounds ! From previous (non-MVA) analysis:
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Photon ID MVA ! Garden variety classification BDT
! Shower shape variables ! Isolation variables ! Underlying event activity
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! Cluster rapidity
Barrel, Mγγ>160 GeV
Endcap, Mγγ>160 GeV
Final Squeeze: putting everything together into the classification MVA ! Kinematics
! pTγ/mγγ for both photons
! pesudo-rapidities of both photons ! cosine of opening angle in azimuthal plane
! Instrumental background ! Photon ID BDT values for both photons
! Mass resolution ! for correct vertex choice ! for incorrect vertex choice ! probability of the vertex to be chosen correctly
! Events need to be weighted so that best resolution events get higher classifier values
4/6/12 Yuri Gershtein 51
Final Classification ! Although data-MC agreement is very good, strictly speaking it
is not necessary for background. It just shows that training is close to optimal
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HIG-11-033 results
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Combination
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Combination
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Combination
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