Gunther Roland JET summer school
Jet reconstruction and jet physics in HI collisions with CMS
JET collaboration summer schoolOSU June 2013
Gunther Roland
Gunther Roland JET summer school
Overview
• I will discuss selected aspects of jet finding and jet physics in HI collisions using CMS
• Emphasis on tools, algorithms, analysis methods, not on physics - look behind the curtain
• What are the main experimental challenges?• Caveats for comparison to theory• What can one expect in the future?
2
Gunther Roland JET summer school4
Inner tracker:charged particlesvertex, isolation
solenoid
EM and Hadron calorimetersphotons, isolation
MuonHCALECALTracker |η|< 2.5
|η|< 3.0
|η|< 5.2
|η|< 2.4
Pb
Pb
Calojet
Particle Flow Jet (track pT> 0.9GeV/c)
CMS detector
Gunther Roland JET summer school
Trigger and Event Selectionl MinBias Trigger:
l Coincidence of BSC or HF signall Trigger efficiency: 97% ± 3%
• Di-Muon Trigger:l Two tracks in the muon detector
• Photon Trigger:l Uncorrected photon ET> 15 GeV
• Jet Trigger: l Uncorrected jet ET> 35, 50 GeV
• High pT track trigger:l Charged particles pT> 12 GeV/c
l Centrality determination:l Forward calorimeter (HF) energy
Dec. 2011: ≈ 150 µb–1
Dec. 2010: ≈ 7 µb–1
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Gunther Roland JET summer school
CMS Jet Jargon
5
• Some CMS jargon– “gen level”: final state particle truth info of event generators
(e.g. PYTHIA) before decay of long-live particles (cτ > 1cm)
– “reco level”: after simulation of all decays, full simulation (GEANT 4) and reconstruction of tracks, photons, jets etc
– “embedding”: combining a single generator hard scattering event (e.g. a PYTHIA pp dijet event) with the heavy-ion underlying event (from data or from HYDJET). Called “overlay” by ATLAS
– “closure test”: comparison of an observable (e.g. dijet Aj) for e.g. PYTHIA gen level truth vs reco level result after embedding for the same set of PYTHIA events
Gunther Roland JET summer school
Jets and underlying event
2
?
partons+Underlying Event(UE)
hadrons:parton-associatedUE-associatedJETS: well defined by the clustering algorithm, FastJet anti-kT, R = 0.3
Energy-corrected to particle-level jets (PYTHIA) w/o constituent pT threshold
Gunther Roland JET summer school3
?
Challenges:• Under/over subtraction of UE • Some parton associated particles are lost because of reconstruction inefficiency• Some parton associated particles are lost because of bkg subtraction• Calorimeter energy deposit of the final particles may fluctuate• Particle composition may be different from assumptions used for energy scale calibration
Jets and underlying event
Gunther Roland JET summer school
HCAL clusters•Neutral hadrons•Capture charged hadrons that tracking missed•Event-by-event shower fluctuations:
• Non-linearity• Wide resolution
•Acceptance limited due to B-field•Low granularity
4T
5
ECAL clusters•Photons, electrons
Tracks•Best pT resolution•Blind to neutral energy•Not 100% efficient•Limited acceptance (Details: CMS-PAS-HIN-11-004)
CMS Physics objects
Gunther Roland JET summer school
Jet Measurements
(Tracking for only the primary vertex)
UE subtraction and
jet clustering
ParticleFlow algorithm
Towers
6
Δη x Δϕ0.076 x 0.076
in barrel
Calorimeter clusters and tracks are matched(Details: CMS-PAS-HIN-11-004)
Candidates are merged into pseudo-towers for UE subtraction(Energy calibration is done for UE-subtracted found jets)
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UE-subtraction algorithm
Tunable parameters:• Coefficient of RMS
Estimate background for each tower ring of constant η estimated background = <pT> + σ(pT)•Captures dN/dη of background•Misses ϕ modulation – to be improved
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Subtract background from all towersRun the clustering algorithm (anti-kT)
UE-subtraction algorithm
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Start over, knowing where the jets roughly are
UE-subtraction algorithm
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Start over, knowing where the jets roughly areExclude a certain area around the jets Re-estimate the background for all towers
Tunable parameters:•Coefficient of RMS•Raw jet threshold•Radius of exclusion(not necessarily = R)
UE-subtraction algorithm
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Start over, knowing where the jets roughly areExclude a certain area around the jets Re-estimate the background for all towersSubtract final backgroundCluster jets
Tunable parameters:•Coefficient of RMS•Raw jet threshold•Radius of exclusion(not necessarily = R)
UE-subtraction algorithm
Gunther Roland JET summer school
Limits on low pT jet finding
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Jets that fluctuated below threshold in first step of iteration(got added to UE estimate, leading to oversubtraction)
Gunther Roland JET summer school
Tracking performance
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Validate efficiency by:
•Analysis of hadron spectra in pp•Track multiplicity distributions•CaloTower-track matching•CaloJet-PFJet matching
Important to understand in jet-track correlation analyses
•Tracking effiency vs distance to jet axis•Autocorrelations
CMS-PAS-HIN-12-013
Gunther Roland JET summer school
• Hydjet 1.8 default tune successfully reproduces:• Charged hadron multiplicity• Charged hadron pT spectrum• Azimuthal asymmetry of low-pT particles (Elliptic Flow)
• Pythia dijets embedded into Hydjet and fully simulated
http://lokhtin.web.cern.ch/lokhtin/hydro/plots
-- Hydjet•ALICE PRL106(2011)032301 0-5% Central
-- Hydjet•ALICE PLB 696 (2011) 30 0-5% Central
-- Hydjet•ALICE PRL 105 (2010) 252302 10-20% Central
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Effects in the reconstruction:•Tracks : Efficiency, fakes•Jets : Energy scale, resolution, fake-jets
Understand jet performance in HI
(GeV/c)TJetp
100 150 200 250 300
> (G
eV/c
)T
< Ba
ckgr
ound
p
0
20
40
60
80
100PbPb data
0-5% 5-10% 10-30% 30-50% 50-70% 70-90% PYTHIA + HYDJET
Particle Flow Jets R = 0.3TAnti-k| < 2
Jetη|
CMS Preliminary(a)
-1bµ dt = 129 PbPb L∫
(GeV/c)TJetp
100 150 200 250 300
) (G
eV/c
)T
RM
S ( B
ackg
roun
d p
0
5
10
15
20 PbPb data0-5% 5-10% 10-30% 30-50% 50-70% 70-90% PYTHIA + HYDJET
Particle Flow Jets R = 0.3TAnti-k| < 2
Jetη|
CMS Preliminary(b)
-1bµ dt = 129 PbPb L∫
Gunther Roland JET summer school
Jet background in PbPb
• Mean and width for background pT subtracted from jets– before jet energy correction
• Again, data and MC agree
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PbPb
PbPb
Gunther Roland JET summer school
Jet energy scale and resolution
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See: CMS-PAS-HIN-12-004
• Corrections derived from PYTHIA (pp) simulations• Resolution increases (N term – see later slide) by ~ 5 GeV with centrality• Energy scale shifted by ~2-1% due to subtraction of low-pT
• Dependence on other properties (parton-type, fragmentation) are examined to evaluate systematic uncertainties
Gunther Roland JET summer school
Jet energy smearing
Unfolding vs smearing for data/MC comparison
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For 30 GeV jets : C component ~ 0.7 GeV, S ~ 6.5 GeV, N ~ 5 GeV
For 120 GeV jets : C component ~ 3 GeV, S ~ 13 GeV, N ~ 5 GeV
From PLB 718 (2013) 773 : Photon events
Not exactly inclusive jet resolution
Gunther Roland JET summer school
Modified PYTHIA quark fraction
Standard PYTHIA
Some systematic checks
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Fully simulated events are studied for determination of systematics
MODIFIED PYTHIA: Parton fractions are selected/reweighted to bias the sample for a specific type of hard fragmentation
PYQUEN: Different tunes with Radiational/Collisional energy loss
Gunther Roland JET summer school
Quark vs gluon jet responseCMS-PAS-HIN-11-004
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JEC is done assuming PYTHIA quark/gluon fractionChange in this ratio (e.g. “gluon filtering”) will bias JEC
Gunther Roland JET summer school
Dijet balance centrality evolution from 2011 data set
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DIjet asymmetries
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Dijet imbalance vs pT
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• Dijets in reference (pp, PYTHIA) more balanced with increasing pT
• <pT,2/pT,1> in PbPb consistent with constant offset from reference MC
Gunther Roland JET summer school
Caveats
• The order of cuts is important for defining measurement– e.g. rapidity cuts and leading jet selection don’t commute
• When smearing MC jets for data comparison, order of smearing, cuts, selection is critical– smearing may flip order of leading and subleading jet!!!
• Careful reading of papers, but probably also direct communication is often useful...
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Impact of jet energy smearing
0-20%
Jet energy smearing
Swapped leading and subleading jet
Subleading jet out of acceptance
Generator level leading and subleading jets matches reco level
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l Need realistic MC generator (for both jet and UE)l Iterative feedback cycle is very important (like PYTHIA v.s pp data
in high energy community)l CMS is willing to use and check if you offer a jet event generator
l To compare with CMS data: Need to include reconstruction effect properly
l Genjet à energy loss à apply smearing
Inputs to the MC discussion
Generator Experiment
Used to derive correction and compare with data
Feedback and improve the generator
Jet MC and data comparison
Gunther Roland JET summer school
• Azimuthal decorrelation:|ΔφJγ|, and its parametrized width σ(|ΔφJγ|)
• Transverse momentum ratio:xJγ = pT
Jet/pT γ, and its mean ⟨xJγ⟩
• Fraction of photons with associated jets: RJγ
• pT γ > 60 GeV/c (to have sufficient xJγ phase space)
• pTJet > 30 GeV/c (constrained by efficiency)
pTγ
|ΔφJγ|
pTJet
Photon-Jet Observables
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Photon Candidates
Anti-kT 0.3 pile-up subtracted
Jet candidates |η|<1.6
Z to eeCross check the
energy correction
Photon Candidates- AllJet
Super Clustersspike rejection
Passing loose photon IDH/E<0.1
Photon isolationEnergy correction
Shower shapeSignal likeσηη<0.01
Background clusters(Fakes from jet)
Shower shapeBackground like0.011<σηη<0.017
Shower shapeSignal likeσηη<0.01
Selected Jet candidates
Selected Jet
Exclude photon candidate |ΔR| > 0.3 w/r to leading photon
Electron rejectionRemaining contribution
1-3%
Leading Cluster
Background template
Photon Candidates- All JetSignal region
Photon-Jet correlations in MIXED events
Event Mixing|Δφ| > 7π/8
PurityDetermination
Dijet Background subtractionPhoton- All JetSignal region Photon-Jet
Final result
PurityDetermination
Photon-Jet Analysis Flow
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• SumIso = uncorrected Track + ECAL + HCAL ET in R < 0.4• GenIso = generator level particle energy in R < 0.4• Isolated prompt (non-decay) photons with SumIso < 1 GeV• Comparison to MC definition GenIso < 5 GeV• SumIso ≠ GenIso due to PbPb underlying event fluctuation
γ
Isolated Isolated Non-isolated
Leading order Higher orders
γ
γ
Final state not
differentiableISOLATED ISOLATED NON-ISOLATED
Isolated Photons
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Gunther Roland JET summer school
• Shower shapeσηη = ∑i
5×5 wi (ηi − η5×5)²/∑i5×5 wi
wi = max(0, c + ln Ei/E5×5)
• Signal photons selected by cutting on σηη < 0.01
• Decay photons contribution determined by fit of (signal + background templates in σηη
• Background σηη template found using data from photons failing the SumIso cuts
Rejection of Decay Photons
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Jet Finding in PbPb
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• Anti-kT particle flow jets, R = 0.3
• UE estimation/subtraction using φ-rings in η, excluding jet candidates (two iterations)
• Reconstruction > 90% efficient for pTJet > 30 GeV/c in
PbPb
Gunther Roland JET summer school
Limits on low pT jet finding
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Jets that fluctuated below threshold in first step of iteration(got added to UE estimate, leading to oversubtraction)
Gunther Roland JET summer school
Photon Candidates
Anti-kT 0.3 pile-up subtracted
Jet candidates |η|<1.6
Z to eeCross check the
energy correction
Photon Candidates- AllJet
Super Clustersspike rejection
Passing loose photon IDH/E<0.1
Photon isolationEnergy correction
Shower shapeSignal likeσηη<0.01
Background clusters(Fakes from jet)
Shower shapeBackground like0.011<σηη<0.017
Shower shapeSignal likeσηη<0.01
Selected Jet candidates
Selected Jet
Exclude photon candidate |ΔR| > 0.3 w/r to leading photon
Electron rejectionRemaining contribution
1-3%
Leading Cluster
Background template
Photon Candidates- All JetSignal region
Photon-Jet correlations in MIXED events
Event Mixing|Δφ| > 7π/8
PurityDetermination
Dijet Background subtractionPhoton- All JetSignal region Photon-Jet
Final result
PurityDetermination
Photon-Jet Analysis Flow
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Δφ
Photon-Jet
Photon–UE Combinatorics
Background Photon–UE Combinatorics
Background Photon–Jet
Signal region
Estimated from event mixing using min-bias data
Data, cf. Slide 13
Background Subtraction, Part I
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Gunther Roland JET summer school
Photon Candidates
Anti-kT 0.3 pile-up subtracted
Jet candidates |η|<1.6
Z to eeCross check the
energy correction
Photon Candidates- AllJet
Super Clustersspike rejection
Passing loose photon IDH/E<0.1
Photon isolationEnergy correction
Shower shapeSignal likeσηη<0.01
Background clusters(Fakes from jet)
Shower shapeBackground like0.011<σηη<0.017
Shower shapeSignal likeσηη<0.01
Selected Jet candidates
Selected Jet
Exclude photon candidate |ΔR| > 0.3 w/r to leading photon
Electron rejectionRemaining contribution
1-3%
Leading Cluster
Background template
Photon Candidates- All JetSignal region
Photon-Jet correlations in MIXED events
Event Mixing|Δφ| > 7π/8
PurityDetermination
Dijet Background subtractionPhoton- All JetSignal region Photon-Jet
Final result
PurityDetermination
Photon-Jet Analysis Flow
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Gunther Roland JET summer school
Statistical Subtraction
Δφ
Background Photon–Jet
Signal region
Estimated from shower shape sideband & purity
Photon-Jet
Background Subtraction, Part II
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Double-subtraction yields photon-associated jet correlation
Gunther Roland JET summer school
• Distribution is consistent with pp & PYTHIA tune Z2 + Hydjet
• To quantify the centrality dependence, peak region is fit with an empirical formula
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Photon-Jet Angular Correlation
γJφΔ
0.0001 1.0473 2.0944 3.1416
Pair
Frac
tion
-210
-110
1PbPb DataPYTHIA + HYDJETpp Data
50% - 100%
(a)
0 π31 π3
2
γJφΔ γJ
φΔ0.0001 1.0473 2.0944 3.1416
Pair
Frac
tion
-210
-110
1=2.76TeV NNs
-1bµ L dt = 150 ∫30% - 50%
(b)
0 π31 π3
2
γJφΔ γJ
φΔ0.0001 1.0473 2.0944 3.1416
Pair
Frac
tion
-210
-110
1| < 1.44γη > 60GeV/c |
Tγp
| < 1.6Jetη > 30GeV/c |TJetp
10% - 30%
(c)
0 π31 π3
2
γJφΔ γJ
φΔ0.0001 1.0473 2.0944 3.1416
Pair
Frac
tion
-210
-110
1CMS Preliminary
0% - 10%
(d)
0 π31 π3
2 π
γJφΔ
Gunther Roland JET summer school
Angular width σ(|ΔφJγ|) is consistent, both PbPb to pp and PbPb to PYTHIA tune Z2 + HYDJET
48
Photon-Jet Angular Correlation
partN0 100 200 300 400
) γJφΔ(
σ
0
0.050.1
0.15
0.2
0.250.3
0.35
0.4
0.450.5
(a)
CMS Preliminary
PbPb DataPYTHIA + HYDJETpp DataPYTHIA
| < 1.44γη > 60GeV/c |Tγp
| < 1.6Jetη > 30GeV/c |TJetp
Gunther Roland JET summer school
• Momentum ratio shifts/decreases with centrality• Unitary normalized distribution, points anticorrelated• Red/blue boxes try to indicate possible, anticorrelated
systematic variation
49
Photon-Jet Momentum Balance
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Photon-Jet Momentum Balance
photon-jet momentum balance shifts in central events(relative to PYTHIA reference, calibrated in 7GeV pp)
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Photon-Jet Momentum Balance
Reasonable agreement between ATLAS and CMSNOTE: CMS correlates photon w/ associated jet, ATLAS w/ leading jet
partN0 100 200 300 400
γJR
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1CMS(b)
π87 >
γJφΔ
NOTE: ATLAS data scaled such that references match
Gunther Roland JET summer school
• Azimuthal decorrelation:|ΔφJγ|, and its parametrized width σ(|ΔφJγ|)
• Transverse momentum ratio:xJγ = pT
Jet/pT γ, and its mean ⟨xJγ⟩
• Fraction of photons with associated jets: RJγ
• pT γ > 60 GeV/c (to have sufficient xJγ phase space)
• pTJet > 30 GeV/c (constrained by efficiency)
pTγ
|ΔφJγ|
pTJet
Example: photon-jet closure test
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Gunther Roland JET summer school
Example: photon-jet closure test
53
For this analysis, the effect of background fluctuations, UE background subtraction, jet
finding, photon isolation, background jets etc etc etc is fully accounted for by simple Gaussian
smearing of jet energy
Gunther Roland JET summer school
Closure test for modified jets (PYQUEN)
54
For this analysis, the effect of background fluctuations, UE background subtraction, jet finding, photon
isolation, background jets etc etc etc is fully accounted for by simple Gaussian smearing of jet energy
True also for models that show a strong quenching effect (e.g. PYQUEN)
Gunther Roland JET summer school
Missing-pT||
arXiv:1102.1957 [nucl-ex]
0-30% Central PbPb
balanced jets unbalanced jets
Missing pT||:
excess away from leading jet
excess towards leading jet
Calculate projection of pT on leading jet axis and average over selected tracks with pT > 0.5 GeV/c, |η| < 2.4
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Gunther Roland JET summer school
Missing-pT||
arXiv:1102.1957 [nucl-ex]
0-30% Central PbPb
balanced jets unbalanced jets
Missing pT||:
excess away from leading jet
excess towards leading jet
Integrating over the whole event final state,momentum balance is restored
arXiv:1102.1957 [nucl-ex]
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Gunther Roland JET summer school
Missing-pT||
arXiv:1102.1957 [nucl-ex]
0-30% Central PbPb
in-cone
out-of-cone
balanced jets unbalanced jets
In-ConeΔR<0.8
Out-of-ConeΔR<0.8
arXiv:1102.1957 [nucl-ex]
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Gunther Roland JET summer school
Missing-pT||
arXiv:1102.1957 [nucl-ex]
in-cone
out-of-cone
In-ConeΔR<0.8
Out-of-ConeΔR<0.8
Momentum balance in the event is carried by low momentum particles at large angles to jets
arXiv:1102.1957 [nucl-ex]
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More differential measurement?
• 2011 data should allow more differential measurement – vs asymmetry, dR, jet pT
• Method requires uncorrelated UE to cancel to 0.1%– 10000 tracks x 0.5GeV pT each– desired resolution is ~5GeV in missing pT
• Tracking efficiency depends on– particle pT, mass, eta, phi– vertex location, event multiplicity, local multiplicity density– vicinity to jet, jet pT,...
• More differential measurement requires– improvement in tracking efficiency from ~70% to ~ 85-90%– improved jet UE subtraction to reduce jet-UE correlation
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Fragmentation functions• Distribution of associated track pT in cone, relative to
measured jet pT
– plot Z = pT, track/pT,jet cos(ΔR), ξ = ln(1/z) and track pT
– No direct connection to parton pT at scattering
•
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Fragmentation functions
• Distribution of associated track pT in cone, relative to measured jet pT
– plot Z = pT, track/pT,jet cos(ΔR), ξ = ln(1/z) and track pT
– No direct connection to parton pT at scattering
• “Associated tracks” are defined relative to track population in same-event displaced cone– ATLAS: “cone grid”; CMS: “eta-reflected cone”– No distinction between in-cone “associated tracks”
from medium response or from hard-parton fragmentation
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Fragmentation functions
pp smeared and reweighted to match PbPb jet pT spectrum
Gunther Roland JET summer school
z
19
Jet fragmentation functions
z • The hard part of the fragmentation is slightly modified
• May affect calorimeter-related resolution• Effects can be estimated by
• Modified Pythia parton content• Various Pyquen tunes
• There appears to be an enhanced soft component
- May interfere with PU subtraction to affect energy scale- Bias towards upwards fluctuations in UE
• Effects can be estimated by• Embedding tracks into jets• Various Pyquen tunes
CMS-PAS-HIN-12-013
Gunther Roland JET summer school
Fragmentation Function in Data
Pb Pb PbPb
• Particle Flow Jet Reconstruction– Anti kT, R=0.3– Same background subtraction as
calorimeter based jets– Fully efficient for pT > 40GeV/c– Good control of jet pT scale– Applied in pp and PbPb
• Dijet selection– pT
Jet1>100GeV/c– pT
Jet2>40GeV/c– Δφ12>2π/3
• Compare Leading and Subleading Jet– Select Tracks in ΔR=0.3 cone– pT> 4 GeV/c
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Selection of b-jet Results from CMSIdentification of b-quark jets with the CMS experiment CMS Physics Analysis Summary BTV-11-004
Inclusive b-jet production in pp collisions at √s = 7 TeV JHEP 1204 (2012) 084, arXiv:1202.4617Measurement of BB Angular Correlations based on Secondary Vertex Reconstruction at √s = 7 TeV JHEP 1103 (2011) 136, arXiv:1102.3194Measurement of the b-jet to inclusive jet ratio in PbPb and pp collisions at √sNN = 2.76 TeV with the CMS detector CMS Physics Analysis Summary HIN-12-003
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Secondary Vertex Mass Fits
17
• After enriching sample in b-jets with the SSVHE tagger, we fit the SV mass distribution
• Shapes of b and non-b templates taken from MC, normalizations allowed to float
• The shapes of the non-b templates are cross-checked with a data-driven method
• The stability of the fits and the shapes of the templates are the dominant sources of systematics uncertainty )2Secondary vertex mass (GeV/c
0 1 2 3 4 5 6
Num
ber o
f jet
s10
210
310
410
510
)2Secondary vertex mass (GeV/c0 1 2 3 4 5 6
Num
ber o
f jet
s10
210
310
410
510PbPb datab-jet templatec-jet templateusdg-jet template
< 100 GeV/cT
80 < p
/NDF = 10.1 / 112χ
CMS Preliminary = 2.76 TeVNNs
b-tagged sample
(GeV/c)T
Jet p80 100 120 140 160 180 200
b-je
t fra
ctio
n0
0.01
0.02
0.03
0.04
0.05
0.06CMS Preliminary = 2.76 TeVs
-1 L dt = 231 nb∫pp DataPythiaSyst. uncertainty
(GeV/c)T
Jet p80 100 120 140 160 180 200
b-je
t fra
ctio
n0
0.01
0.02
0.03
0.04
0.05
0.06
(GeV/c)T
Jet p80 100 120 140 160 180 200
b-je
t fra
ctio
n
0
0.01
0.02
0.03
0.04
0.05
0.06CMS preliminary = 2.76 TeVNNs
Centrality 0-100%
-1bµ L dt = 150 ∫PbPb DataPythia+HydjetSyst. uncertainty
(GeV/c)T
Jet p80 100 120 140 160 180 200
b-je
t fra
ctio
n
0
0.01
0.02
0.03
0.04
0.05
0.06
Gunther Roland JET summer school
b-Tagging Purity and Efficiency
18
Purity: b-jet fraction in SV tagged sample extracted from SV mass fit
Efficiency: Fraction of b-jets which are tagged by their SV
• Efficiency is extracted from simulation and with a data-driven method using the JP tagger, i.e., w/o requiring a SV
• For both efficiency and purity, MC is fairly close to data “out of the box”
PbPb pp
partN0 50 100 150 200 250 300 350 400
b-je
t fra
ctio
n
0
0.01
0.02
0.03
0.04
0.05
0.06CMS preliminary = 2.76 TeVNNs
< 100 GeV/cT
80 < Jet p
-1bµ L dt = 150 ∫PbPb DataPythia+HydjetSyst. uncertainty
50-100% 20-50% 0-20%
partN0 50 100 150 200 250 300 350 400
b-je
t fra
ctio
n
0
0.01
0.02
0.03
0.04
0.05
0.06
Gunther Roland JET summer school
b-jet Fraction vs. Centrality
20
b-jet fraction does not show a strong centrality dependence
Gunther Roland JET summer school
Summary
• Fantastic set of new results on jets from LHC and RHIC• In general, good consistency between experiments• Interface to theory needs to be refined:
– unfolding?– folding?
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Underlying Event Subtraction (CMS)
η
φ
1. <ET> calculated in strips of η. Subtract <ET> + σ
η
φ
2. Run anti-kT algorithm on background-subtracted towers
η
φ
3. Exclude reconstructed jetsand re-estimate background
η
φ
4. Re-run anti-kT algorithm to get final jets
For details see:•CMS, arXiv:1102.1957•Kodolova et al., EPJC 50 (2007) 117