Jet Energy Corrections in CMS

1. Jet Energy Corrections in CMS Daniele del Re Universita’ di Roma “La Sapienza” and INFN Roma

2. Outline • 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 Daniele del Re (La Sapienza & INFN)

3. CMS Detector: Calorimetry >75k lead tungstate crystals crystal lenght ~23cm Front face 22x22mm2 PbWO4 30g/MeV X0=0.89cm HO Had Barrel: HB brass Absorber and Had Endcaps: HE scintillating tiles+WLS Had Forward: HF scintillator “catcher”. Had Outer: HO iron and quartz fibers HB HE HF Daniele del Re (La Sapienza & INFN)

4. Jet 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) Daniele del Re (La Sapienza & INFN)

5. Parton 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 Daniele del Re (La Sapienza & INFN)

6. Causes 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 quarkor gluon Daniele del Re (La Sapienza & INFN)

7. Dependence of bias • vs pT of jet • Non-compensating calorimeter • low pT tracks in jet • vs segmentation • large effect vs pseudorapidity h (large detector variations) • small effect vs f (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 Daniele del Re (La Sapienza & INFN)

8. Dependence of bias vs causes Complicated grid: better to estimate dependences from data than study each single effect Daniele del Re (La Sapienza & INFN)

9. Factorization 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) Daniele del Re (La Sapienza & INFN)

10. Levels of corrections • Offset:removal of pile-up and residual electronic noise. • Relative (h):variations in jet response with h relative to control region. • Absolute (pT):correction to particle level versus jet pT in control region. • EM fraction: correct for energy deposit fraction in em calorimeter • Flavor:correction to particle level for different types of jet (b, t, etc.) • Underlying Event:luminosity independent spectator energy in jet • Parton:correction to parton level L1 Offset L2 Rel:h L3 Abs:pT L4 EMF L5 Flavor L1 UE L1 Parton Reco Jet Calib Jet Required Optional Daniele del Re (La Sapienza & INFN)

11. Level 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 Daniele del Re (La Sapienza & INFN)

12. Level 1 Correction Evaluate effect of red blobs without ZS in data taking 1) take runs without SR-ZStriggered 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 Under threshold: removed by ZS Now over threshold: not removed Daniele del Re (La Sapienza & INFN)

13. Level 2: h dependence Goal: flatten relative response vs h • extract relative jet response with respect to barrel • barrel has larger statistics • better absolute scale • small dep. vs h • extract • h correction in bins of pT (fully uncorrelated with the next L3 correction) Relative Response Before 1 After 2 1 3 4 Jet h Daniele del Re (La Sapienza & INFN)

14. Level 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 Probe Jet “other jet” y Trigger Jet |η|<1.0 z Probe Jet “other jet” y x Trigger Jet |η|<1.0 Daniele del Re (La Sapienza & INFN)

15. Level 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 Daniele del Re (La Sapienza & INFN)

16. Level 3: pT dependence y x Goal: flatten absolute response variation vs pT • Balance on transverse plane (similar to L2 case), two methods: • g + 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) Daniele del Re (La Sapienza & INFN)

17. Level 3: g+jet example • main bkg: QCD events (di-jet) • selection based on • g isolation from tracks, other em and had. deposits • per event selection: reject events with multiple jets, g 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) pT(jet)/pT(g) Daniele del Re (La Sapienza & INFN)

18. Level 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 (h, pT) • introducing em fraction correction improves resolution Daniele del Re (La Sapienza & INFN)

19. Level 4: extract corrections • start with MC corrections • idea is to use large g+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. Daniele del Re (La Sapienza & INFN)

20. 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 Daniele del Re (La Sapienza & INFN)

21. 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 g bb, h gt t, 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 • g+jets, using b tagging • pp→bbZ, with Z→ll Daniele del Re (La Sapienza & INFN)

22. 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 Daniele del Re (La Sapienza & INFN)

23. Level 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, ...) Daniele del Re (La Sapienza & INFN)

24. Sanity 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 g+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 g+jet sample Daniele del Re (La Sapienza & INFN)

25. Plan 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 • numbers do not take into account • low pT: low resolution, larger backgrounds • larger uncertainties • 2) large pT: control samples have low • cross section •  larger stat. needed Daniele del Re (La Sapienza & INFN)

26. Conclusions • CMS proposes a fixed sequence of factorized corrections • experience from previous experiments guided this plan • first three levels: noise-pileup, vs h 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 partonare 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 Daniele del Re (La Sapienza & INFN)