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

2. 02/19/07Daniele del Re (La Sapienza & INFN)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

3. 02/19/07Daniele del Re (La Sapienza & INFN)3 CMS Detector: Calorimetry 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 HO HF >75k lead tungstate crystals crystal lenght ~23cm Front face 22x22mm 2 PbWO 4 30  /MeV X 0 =0.89cm

4. 02/19/07Daniele 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 k T –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)

5. 02/19/07Daniele 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

6. 02/19/07Daniele 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 quark or gluon

7. 02/19/07Daniele del Re (La Sapienza & INFN)7 Dependence of bias vs p T 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

8. 02/19/07Daniele del Re (La Sapienza & INFN)8 Dependence of bias vs causes Jet algorithm Non-compensating Segmentation Material infront of cal. Electronicnoise Physics noise Originalquark/gluon vs p T vs  vs em fraction vs flavor vs conditions vs process Complicated grid: better to estimate dependences from data than study each single effect

9. 02/19/07Daniele 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)

10. 02/19/07Daniele del Re (La Sapienza & INFN)10 Levels 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 (p T ): correction to particle level versus jet p T 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 L2 Rel:  L1 Offset L3 Abs:pT L4 EMF L5 Flavor L1 UE L1 Parton Reco Jet Calib Jet Required Optional

11. 02/19/07Daniele 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

12. 02/19/07Daniele del Re (La Sapienza & INFN)12 Level 1 Correction 1) take runs without SR-ZS triggered with jets –perform pedestal subtraction –evaluate the effect of SR-ZS vs p T  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 Under threshold: removed by ZS Now over threshold: not removed

13. 02/19/07Daniele del Re (La Sapienza & INFN)13 Level 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 p T (fully uncorrelated with the next L3 correction) 1 Before After 13 2 Jet  4 Relative Response

14. 02/19/07Daniele del Re (La Sapienza & INFN)14 Level 2: data driven with p T 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” Trigger Jet |η|<1.0 y y z x

15. 02/19/07Daniele del Re (La Sapienza & INFN)15 Level 2: Missing Projection Function MPF: p T 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 E T –need to understand whole calorimeter before it can be used Response ratio extracted as

16. 02/19/07Daniele del Re (La Sapienza & INFN)16 Level 3: p T 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 p T – 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

17. 02/19/07Daniele del Re (La Sapienza & INFN)17 Level 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 p T range –but for low p T large contamination from QCD (use of Z+jet there) p T (jet)/p T (  )

18. 02/19/07Daniele 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 ( , pT) introducing em fraction correction improves resolution

19. 02/19/07Daniele del Re (La Sapienza & INFN)19 Level 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.

20. 02/19/07Daniele 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

21. 02/19/07Daniele 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  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

22. 02/19/07Daniele 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

23. 02/19/07Daniele 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 p T –dependent on MC generators (parton shower models, PDF,...)

24. 02/19/07Daniele 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  +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

25. 02/19/07Daniele 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 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) 1)low p T : low resolution, larger backgrounds   larger uncertainties 2) large p T : control samples have low cross section  larger stat. needed

26. 02/19/07Daniele 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  and vs p T 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