jet energy corrections in cms daniele del re universita’ di roma “la sapienza” and infn roma
TRANSCRIPT
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