Jet Energy Corrections in CMS

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


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


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)


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


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


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


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


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)


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


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


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


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


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


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


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 ,,/)(


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

()


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


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.


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


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


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


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, ...)


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


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


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

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Jet Energy Corrections in CMS

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