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Jet Energy Corrections in CMSJet Energy Corrections in CMS
Daniele del Re
Universita’ di Roma “La Sapienza” and INFN Roma
02/19/0702/19/07 Daniele del Re (La Sapienza & INFN) Daniele del Re (La Sapienza & INFN) 22
OutlineOutline
• Summary of effects to be corrected in jet reconstruction
• CMS proposal: factorization of corrections
• data driven corrections– Strategy to extract each correction factor from data
• Perspectives for early data – Priorities, expected precisions, statistics needed
Note: results and plots in the following are preliminary and not for public use yet
02/19/0702/19/07 Daniele del Re (La Sapienza & INFN) Daniele del Re (La Sapienza & INFN) 33
CMS Detector: CalorimetryCMS Detector: Calorimetry
Had Barrel: HB brass Absorber and Had Endcaps: HE scintillating tiles+WLSHad Forward: HF scintillator “catcher”. Had Outer: HO iron and quartz fibers HB
HE
HO
HF
>75k lead tungstate crystalscrystal lenght~23cm
Front face22x22mm2
PbWO4
30/MeVX0=0.89cm
02/19/0702/19/07 Daniele del Re (La Sapienza & INFN) Daniele del Re (La Sapienza & INFN) 44
Jet reconstruction and calibrationJet reconstruction and calibration
• Calorimeter jets are reconstructed using towers:– Barrel: un-weighted sum of energy deposits in
one or more HCAL cells and 5x5 ECAL crystals
– Forward: more complex HCAL-ECAL association
• In CMS we use 4 algorithms: iterative cone, midpoint cone, SIScone and kT
– will give no details on algorithms, focusing on corrections
• Role of calibration:
correct calorimeter jets back either to particle or to parton jets (see picture)
02/19/0702/19/07 Daniele del Re (La Sapienza & INFN) Daniele del Re (La Sapienza & INFN) 55
Parton level vs particle level correctionsParton level vs particle level corrections
• In CMS – Calojets are jets reconstructed from calorimeter energy deposits with a
given jet algorithm
– Genjets are jets reconstructed from MC particles with the same jet algorithm
• Two options– convert energy measured in jets back to partons (parton level)
– convert energy measured in jets back to particles present in jet (particle level)
• Idea is to correct back to particle level (Genjets)
• Parton level corrections are extra and can be applied afterwards
02/19/0702/19/07 Daniele del Re (La Sapienza & INFN) Daniele del Re (La Sapienza & INFN) 66
Causes of bias in jet reconstructionCauses of bias in jet reconstruction
• jet reconstruction algorithm– Jet energy only partly reconstructed
• non-compensating calorimeter– non-linear response of calorimeter
• detectors segmentation • presence of material in front of calorimeters and magnetic
field• electronic noise • noise due to physics
– Pileup and UE
• flavor of original quark or gluon
02/19/0702/19/07 Daniele del Re (La Sapienza & INFN) Daniele del Re (La Sapienza & INFN) 77
Dependence of bias Dependence of bias
• vs pT of jet – Non-compensating calorimeter– low pT tracks in jet
• vs segmentation – large effect vs pseudorapidity (large detector variations)– small effect vs (except for noisy or dead cal towers)
• vs electromagnetic energy fraction– non-compensating calorimeter
• vs flavor• vs machine and detector conditions• vs physics process
– e.g. UE depends on hard interaction
02/19/0702/19/07 Daniele del Re (La Sapienza & INFN) Daniele del Re (La Sapienza & INFN) 88
Dependence of bias vs causesDependence of bias vs causes
Jet algorithm
Non-com
pensating
Segm
entation
Material in front of
cal.
Electronic noise
Physics noise
Original quark/gluon
vs pT
vs vs em fraction
vs flavor
vs conditions
vs processComplicated grid: better to estimate dependences from data than study each single effect
02/19/0702/19/07 Daniele del Re (La Sapienza & INFN) Daniele del Re (La Sapienza & INFN) 99
Factorization of correctionsFactorization of corrections
• correction decomposed into (semi)independent factors applied in a fixed sequence– choice also guided by experience from previous experiments
• many advantages in this approach:– each level is individually determined, understood and refined– factors can evolve independently on different timescales– systematic uncertainties determined independently– Prioritization facilitated: determine most important corrections
first (early data taking), leave minor effects for later– better collaborative work– prior work not lost (while monolithic corrections are either kept
or lost)
02/19/0702/19/07 Daniele del Re (La Sapienza & INFN) Daniele del Re (La Sapienza & INFN) 1010
Levels of correctionsLevels of corrections
1. Offset: removal of pile-up and residual electronic noise.
2. Relative (): variations in jet response with relative to control region.
3. Absolute (pT): correction to particle level versus jet pT in control region.
4. EM fraction: correct for energy deposit fraction in em calorimeter
5. Flavor: correction to particle level for different types of jet (b, , etc.)
6. Underlying Event: luminosity independent spectator energy in jet
7. Parton: correction to parton level
L2Rel:
L1Offset
L3Abs:pT
L4EMF
L5Flavor
L1UE
L1Parton
RecoJet
CalibJet
Required Optional
02/19/0702/19/07 Daniele del Re (La Sapienza & INFN) Daniele del Re (La Sapienza & INFN) 1111
Level 1: OffsetLevel 1: Offset
Goal: correct for two effects 1) electronic noise 2) physics noise
1) noise in the calorimeter readouts
2a) multiple pp interactions (pile-up)
2b) (underlying events, see later)
• additional complication: energy thresholds applied to reduce data size– selective readout (SR) in em calorimeter (ECAL)
– zero suppression (ZS) in had calorimeter (HCAL)
• with SR-ZS, noise effect depends on energy deposit – need to properly take into account SR-ZS effect before subtracting noise
02/19/0702/19/07 Daniele del Re (La Sapienza & INFN) Daniele del Re (La Sapienza & INFN) 1212
Level 1 CorrectionLevel 1 Correction
1) take runs without SR-ZS triggered with jets– perform pedestal subtraction
– evaluate the effect of SR-ZS vs pT Apply ZS offline and calculate
multiplicative term:
2) take min-bias triggers without SR-ZS– run jets algorithms and determine noise
contribution (constant term):
3) correct for SR-ZS and subtract noise
no pileup and noise
with pileup and noise
Evaluate effect of red blobs without ZS in data taking
)()( offsetEcorrEE cutjetZS
cutjet
corjet
ZSnojet
ZSnojet
cutjetZS EEEcorr /)(
)(offset
Under threshold: removed by ZS
Now over threshold: not removed
02/19/0702/19/07 Daniele del Re (La Sapienza & INFN) Daniele del Re (La Sapienza & INFN) 1313
Level 2: dependenceLevel 2: dependence
Goal: flatten relative response vs
• extract relative jet response with respect to barrel
– barrel has larger statistics
– better absolute scale
– small dep. vs
• extract
• correction in bins of pT (fully
uncorrelated with the next
L3 correction)
barrelT
probeTT pppc /)(),(
1
Before
After
1 32Jet
4
RelativeResponse
02/19/0702/19/07 Daniele del Re (La Sapienza & INFN) Daniele del Re (La Sapienza & INFN) 1414
Level 2: data driven with pT balanceLevel 2: data driven with pT balance
• use of 2→2 di-jet process
• main selection based on– back-to-back jets (x-y)– events with 3 jets removed
• di-jet balance with quantity
• response is extracted with
Trigger Jet |η|<1.0
Probe Jet “other jet”
2/)( barrelT
probeTT PPDijetP
T
barrelT
probeT
DijetP
PPB
Probe Jet “other jet”
Trigger Jet |η|<1.0
y
y
z
x
02/19/0702/19/07 Daniele del Re (La Sapienza & INFN) Daniele del Re (La Sapienza & INFN) 1515
Level 2: Missing Projection FunctionLevel 2: Missing Projection Function
• MPF: pT balance of the full event
• in principle independent on jet algo– purely instrumental effects
– less sensitive to radiation (physics modeling) in the event
... but depends on good understanding of missing ET
– need to understand whole calorimeter before it can be used
• Response ratio extracted as
tagT
tagTT
tag
recoil
p
nE
R
R ˆ1
0 TrecoilT
tagT Epp
02/19/0702/19/07 Daniele del Re (La Sapienza & INFN) Daniele del Re (La Sapienza & INFN) 1616
Level 3: pT dependenceLevel 3: pT dependence
Goal: flatten absolute response variation vs pT
• Balance on transverse plane (similar to L2 case), two methods:– + jet
mainly qg->qy large cross section not very clean at low pT
– Z + jet relatively small cross cleanest
• response is– rescale to parton level, extra MC correction needed from parton to particle
• also MPF method (as for L2 case)
y
x
probeZT
jetTT pppR ,,/)(
02/19/0702/19/07 Daniele del Re (La Sapienza & INFN) Daniele del Re (La Sapienza & INFN) 1717
Level 3: +jet exampleLevel 3: +jet example
• main bkg: QCD events (di-jet)• selection based on
– isolation from tracks, other em and had. deposits
– per event selection: reject events with multiple jets, and jet back-to-back in x-y plane
• ~1 fb-1 enough for decent
statistical error over pT range
– but for low pT large contamination
from QCD (use of Z+jet there)p T
(jet
)/p T
()
02/19/0702/19/07 Daniele del Re (La Sapienza & INFN) Daniele del Re (La Sapienza & INFN) 1818
Level 4: electromagnetic energy fractionLevel 4: electromagnetic energy fraction
Goal: correct response dependence vs relative energy deposit in the two different calorimeters (em and had)
• detector response is different for em particles and hadrons– electrons fully contained in em calorimeter
• fraction of energy deposited by hadrons in em calorimeter varies and change response
• independent from other
corrections (, pT)
• introducing em fraction correction
improves resolution
02/19/0702/19/07 Daniele del Re (La Sapienza & INFN) Daniele del Re (La Sapienza & INFN) 1919
Level 4: extract correctionsLevel 4: extract corrections
• start with MC corrections
• idea is to use large +jet samples (not for early data)
• also possible with di-jet
• in principle used to improve resolution, no effect on bias. Less crucial to have data driven methods.
02/19/0702/19/07 Daniele del Re (La Sapienza & INFN) Daniele del Re (La Sapienza & INFN) 2020
Level 5: flavor Level 5: flavor
Goal: correct jet pT for specific parton flavor
• L3 correction is for QCD mixture of quarks and gluons• Other input objects have different jet corrections
– quarks differ from gluons – jet shape and content depend on quark flavors
• heavy quark very `different from light
– for instance b in 20% of cases decays semileptonically
02/19/0702/19/07 Daniele del Re (La Sapienza & INFN) Daniele del Re (La Sapienza & INFN) 2121
Level 5: data driven extraction Level 5: data driven extraction
• correction is optional– many analyses cannot identify jet flavors, or want special corrections
– correction desired for specialized analysis (top, h bb, h , etc.)
corrections from :
• tt events tt→Wb→qqb– leptonic + hadronic W decay in event, tag 2b jets,
remaining are light quark
– constraints on t and W masses used
to get corrections
• +jets, using b tagging
• pp→bbZ, with Z→ll
02/19/0702/19/07 Daniele del Re (La Sapienza & INFN) Daniele del Re (La Sapienza & INFN) 2222
Level 6: UE Level 6: UE
Goal: remove effect of underlying event
• UE event depends on details of hard scatter
dedicated studies for each process
in general this correction may be not theoretically sound since UE is part of interaction
• plan (for large accumulated stats) is to use same approach as L1 correction but only for events with one reconstructed vertex
02/19/0702/19/07 Daniele del Re (La Sapienza & INFN) Daniele del Re (La Sapienza & INFN) 2323
Level 7: partonLevel 7: parton
Goal: correct jet back to originating parton
• MC based corrections: compare
Calojets after all previous corrections
with partons in bins of pT
– dependent on MC generators
(parton shower models, PDF, ...)
02/19/0702/19/07 Daniele del Re (La Sapienza & INFN) Daniele del Re (La Sapienza & INFN) 2424
Sanity checksSanity checks
given – number of corrections
– possible correlation between corrections
– not infinite statistics in calculating corrections
– smoothing in extracting corrections
sanity checks are needed
• after corrections, re-run +jet balance and check that distribution is flat
• cross-checks between methods should give same answer– e.g. extract corrections from tt and check them on +jet sample
02/19/0702/19/07 Daniele del Re (La Sapienza & INFN) Daniele del Re (La Sapienza & INFN) 2525
Plan for early data takingPlan for early data taking
• day 1: corrections from MC, including lessons from cosmics runs and testbeams
• data<1fb-1: use of high cross-section data driven methods. Tune MC
• longer term: run full list of corrections described so far
Integrated luminosity
Minimum time
Systematic uncertaintiy
10 pb-1 >1 month ~10%
100 pb-1 >6 months ~7%
1 fb-1 >1 year ~5%
10 fb-1 >3 years ~3%
numbers do not take into account 1) low pT: low resolution, larger
backgrounds larger uncertainties
2) large pT: control samples have low cross section larger stat. needed
02/19/0702/19/07 Daniele del Re (La Sapienza & INFN) Daniele del Re (La Sapienza & INFN) 2626
ConclusionsConclusions
• CMS proposes a fixed sequence of factorized corrections– experience from previous experiments guided this plan
• first three levels: noise-pileup, vs and vs pT sub-corrections represent minimum correction for most analyses– priority in determining from data
• EM fraction correction improves resolution
• last three corrections: flavor, UE and parton are optional and analyses dependent
• jet energy scale depends on understanding of detector– very first data will be not enough to extract corrections (rely on MC)– ~1fb-1 should allow to have ~5% stat+syst error on jet energy scale