CMS Jet Reconstruction: Key Aspects and Performance Analysis

1. Jets in CMS Making jets from calorimeter or track information key aspects of the CMS detector relevant for jet reconstruction jet algorithms implemented in the CMS environment Performance of algorithms angular/energy resolutions, timing, mass resolutions, efficiencies Working with jets in physics analyses several topics relevant for a broad discussion Jorgen D’Hondt Vrije Universiteit Brussel – IIHE Jet Workshop – June 20, 2008 – Paris

2. The CMS detector in a nutshell Jet Workshop – Paris June 20, 2008

3. The CMS detector in reality! Installing the beam pipe Jet Workshop – Paris June 20, 2008

4. The CMS detector in reality! Installing the beam pipe Jet Workshop – Paris June 20, 2008

5. The CMS calorimeters HCAL: plastic scintillators brass absorber tower size Dh=0.087 with Df = 5-10o ECAL: PbWO4 crystals EndCap 29x29mm2 Barrel 22x22mm2 ~1o depth ~25X0 Crystals read-out by silicon avalanche photo-diodes (80% of the light collected in the first 25ns) Jet Workshop – Paris June 20, 2008

6. CMS versus ATLAS Rick Cavanaugh Motivation to implement Particle Flow tools for jet reconstruction combining the calorimeter with the tracking system. Jet Workshop – Paris June 20, 2008

7. Input for jet algorithms The general input for jet clustering algorithms are combined ECAL and HCAL “towers”: one or more HCAL cells and the corresponding ECAL cells. A tower is treated as a massless particle and its energy is the sum over all cells if they pass noise thresholds. Overall tower thresholds ET>0.5GeV ~4200 towers in total fraction of towers fired after thresholds in ttbar events. ttbar Jet Workshop – Paris June 20, 2008

8. Jet algorithms in CMS • The CMS software framework supports 4 jet algorithms with several parameter settings: • Iterative Cone: used in the trigger algorithms because it has a short and predictable execution time per event, towers with ET>1GeV are considered as seeds • MidPoint Cone: same seeds requirements as for the Iterative Cone • Seedless-Infrared-Save or SISCone: external code equal to ATLAS (Salam/Soyez) • Fast kT (Cacciari/Salam) • E-scheme for all: the energy and momentum of a jet are defined as the sums of energies and momenta of its constituents Jet Workshop – Paris June 20, 2008

9. Jet algorithms in CMS CMS PAS JME-07-003 To compare: total reconstruction time event ~10s Jet Workshop – Paris June 20, 2008

10. Performance of jet algorithms Performance studies have been done using di-jet QCD and ttbar events. Matching efficiency between a CaloJet and a particle level jet at the vertex, with a matching criteria of DR<0.5. The efficiencies of jets reconstructed with the Fast kT and SISCone algorithms indicate better performance than jets reconstructed with the Midpoint Cone and Iterative Cone algorithms. Jet Workshop – Paris June 20, 2008

11. Performance of jet algorithms The jet response Rjet = pT/pTgen for the barrel region as a function of pTgen is shown for uncorrected jets. Very good agreement between the individual algorithms is found for all regions of the detector, indicating good correspondence between the values of D for the kT algorithm and R for cone algorithms which are being compared. Jet Workshop – Paris June 20, 2008

12. Performance of jet algorithms The hand f resolutions for jets in the barrel region are shown as a function of pTgen. Good agreement is found among all algorithms with comparable radius parameter, with marginal differences at low pTgen. Jets reconstructed with larger radius parameters yield slightly worse resolution both in hand f. Note that the position of the primary vertex is assumed to be at z = 0, which dilutes the hresolution w.r.t. taking the correct position measured with the tracking detectors into account. Jet Workshop – Paris June 20, 2008

13. Performance of jet algorithms The hand f resolutions for jets in the barrel region are shown as a function of pTgen. Good agreement is found among all algorithms with comparable radius parameter, with marginal differences at low pTgen. Jets reconstructed with larger radius parameters yield slightly worse resolution both in hand f. Note that the position of the primary vertex is assumed to be at z = 0, which dilutes the hresolution w.r.t. taking the correct position measured with the tracking detectors into account. Jet Workshop – Paris June 20, 2008

14. Performance of jet algorithms The jet energy resolutions derived from MC truth for jets in the barrel region. Jets reconstructed with Fast kT show slightly worse resolution at low pTgen, while no significant impact of the radius parameter choice is observed. The resolutions are obtained additionally without using MC truth information by using the data-driven Asymmetry Method, which relates the jet pT resolution to the resolution of the pT-imbalance between the two leading jets. Jet Workshop – Paris June 20, 2008

15. Performance of jet algorithms The jet reconstruction performance in ttbar events is studied by selecting events with one (“lepton+jets”) or zero (“alljets”) electron or muon in the final state from a ttbar ALPGEN sample with no additional jets (“ttbar +0 jets”). Only events are considered for which all three decay products of one or both t(tbar) decay(s) can be uniquely matched to reconstructed calorimeter jets. hadronic decays Jet Workshop – Paris June 20, 2008

16. Performance of jet algorithms Jet Workshop – Paris June 20, 2008

17. Performance of jet algorithms Di-jet mass resolutions for Z’ decays are in good agreement (R=0.5). Jet Workshop – Paris June 20, 2008

18. Jets for physics analyses Overview of jet ET cuts applied in Standard Model or related analyses: ttbar (single-lepton) spin correlations : pT>30GeV [CMS Note 2006/111] ttbar FCNC : pT>30-40GeV [CMS Note 2006/93] ttbar (di-lepton) cross section : pTunc>20GeV [CMS Note 2006/77] ttbar (single-lepton) cross section/mass : pT>30GeV [CMS Note 2006/66-64] single-top Wt : pT>35-60GeV [CMS Note 2006/86] veto pT>20GeV single-top t- & s-channel : pT>35GeV [CMS Note 2006/84] W+X (e/m) veto pT>30GeV [CMS Note 2006/61] SUSY/top : pT>30GeV [CMS Note 2006/102] Usualy applied Iterative Cone algorithm (DR=0.5). Pile-Up jets in some analyses reduced by longitudinal primary vertex matching, but no uniform applied procedure. To reduce QCD multi-jet background the pT-cuts on jets have to be increased to about 40GeV for top quark physics. PTDR numbers Jet Workshop – Paris June 20, 2008

19. Optimization of parameters Optimize the matching between the parton and jet kinematics for several benchmark processes (here top quark processes: single-top, top pairs and ttH). Need flexibility of the framework to allow optimization (eg. calibration for several parameters settings). IterCone Les houches hep-ph/0604120 kT MidPoint Jet Workshop – Paris June 20, 2008

20. Comparing algorithms CMS Note 2006/066 Top quark mass analysis in lepton+jet final state. Run three jet algorithms (IC, MC and KT) and compare the directions of the four hard jets (after the event selection) → angular matching Requiring the three algorithms have to match in a to better than 0.3rad, the efficiency of this cut become 76.1% (again after the normal event selection)... unfortunatly not a strong reduction of the influence of jet reconstruction in the top mass systematic uncertainty. a<0.3 85.7% matched 87.5% matched 78.1% matched Jet Workshop – Paris June 20, 2008

21. Pile-up jets Using the primary vertex constraint reduces the amount of jets significant, although they remove generally low pT jets. Current Monte Carlo samples without pile-up collisions, hence few studies in this direction recently. CMS Note 2006/066 lepton+jet (tt) lepton+jet (tt) #jets per event #jets per event after primary vertex constraint after primary vertex constraint Jet Workshop – Paris June 20, 2008

22. Influence of pile-up jets Systematic effects on measurements are usually estimated by turning on/off the extra Pile-Up collisions. The effect on the number of selected events or on the measurant is quoted as an estimate of the systematic uncertainty (often scaled to represent a realistic knowledge of the expected value of Pile-Up collisions). Most of the event selections in proton-proton collisions are based on the presence of jets in the final state. A threshold is often applied on the ET of the reconstructed jets. If Pile-Up was minor effect, it would not change the status of an event between selected or non-selected according to some event selection criteria. But the number of extra Pile-Up collisions is Poissionian distributed E[X]=3.5 (low luminosity) and they can have a sever effect on the reconstruction of an event. E[X]=l Var[X]=l Jet Workshop – Paris June 20, 2008

23. Influence of pile-up jets Study the influence of this randomness ( event-by-event !! ) Simulate 1000 ttbar events (single-lepton) and add low-luminosity Pile-Up collisions according to a random Poisson distributed number (E[X]=3.5)  do this 100 times for each event. Apply an event selection on the reconstructed jets. As we expect 4 hard jets, an ET threshold is applied on the 4th highest ET jet (before jet calibration)  a fraction of the 100 ‘different’ events will be selected The probability for each event to be selected can be determined, where the stochastic part is due to the randomness of the Pile-Up collisions in the hard event. High threshold  low prob Low threshold  high prob Medium thresholds : UNIFORM DISTRIBUTION !! about the thresholds we apply in CMS Jet Workshop – Paris June 20, 2008

24. Influence of pile-up jets Define the variable F to quantify the uniformity of the distribution: F= (1 / #events) . ∑ |Pi - ½| (mean deviation from ½) Determine this variable as a function of the ET-threshold on the 4th jet. (ET>0.5GeV for EcalPlusHcalTowers) Usually jet thresholds are applied around ETcal>30GeV which is about ETrec>15GeV, hence we are very sensitive to the randomness of the Pile-Up collisions. Hence only about 50% of the events would remain selected if one applies a different random sequence of the Pile-Up collisions on the same hard events !! just low-luminosity Pile-Up !! Jet Workshop – Paris June 20, 2008

25. Influence of pile-up jets Going to higher ET-thresholds of course reduces the sample of selected events. The mean probability <Pi> is plotted for an event to be selected as a function of the applied threshold. High threshold reduce the selected event sample, hence this is not the solution. Jet Workshop – Paris June 20, 2008

26. Effect on a simple analysis A cross section can be determined from data events if one knows the efficiency of the event selection criteria, usually from simulation. The efficiency has a statistical uncertainty due to the limited size of the simulation sample, but also from the randomness of the Pile-Up. A robust analysis would have only one peak here De/e~3% Relative uncertainty on cross section (%) The effect of the randomness of the Pile-Up becomes very strong when the selection efficiency is small and only a small sample of events is selected (like in many of our analyses). Jet Workshop – Paris June 20, 2008

27. Leptons influencing jet reconstruction Most analyses have clustered all energy (above thresholds) in the combined EcalPlusHcal calorimeter. This includes also possible isolated and usually hard leptons, or does not include muons. General discussion: should we remove the lepton information (tracker or calorimeter) before applying the jet clustering algorithms. average energy deposite around lepton in top decays (CMS Note 2006/024) hard muon hard electron Ttbar Pile-Up included (ORCA) region to neglect when clustering leptons propagated to calorimeter surface Jet Workshop – Paris June 20, 2008

28. Jet calibration ‘The’ benchmark for a long-term jet calibration effort. Estimated the effect Dmt of a global relative shift (%) on the jet energy scale. For the light quark jets an in-situ calibration has been applied by forcing the W boson mass constraint. For precise top quark mass measurements the JES is very important.  1 GeV effect on mt : D(JES) ≈ 1.5% (for b-jets) (effect of jet resolution to be checked) light heavy CMS Note 2006/066 Jet Workshop – Paris June 20, 2008

29. Jet calibration strategy Factorized approach into natural pieces with additional optional corrections: • Allows a thorough understanding of each individual part of a systematic uncertainty on the jet energy scale (factorized uncertainties). • Most of the factors can be measured directly from collision data: • L1: pile-up and effects of thresholds found in min-bias and zero-bias events. • L2: jet response vs. η relative to barrel found using di-jet balance, etc. • L3: jet response vs. pT found in barrel using g/Z + jets, top, etc. • Lots of work in progress and being put in place for first data later this year. Jet Workshop – Paris June 20, 2008

30. Summary/Outlook • Jets are important in every aspect of the CMS experiment and therefore deserve our full attention • Close collaboration between theoretical and experimental studies is productive • Experiments should be ready to incorporate new algorithms whenever proposed by theoretical arguments • But be aware that before the new algorithm can be applied in physics analyses, lots of work need to be done by the experimentalists. Jet Workshop – Paris June 20, 2008

31. Back-up items Jet Workshop – Paris June 20, 2008

32. ECAL Jet Workshop – Paris June 20, 2008

33. HCAL Jet Workshop – Paris June 20, 2008

34. Jet constituents Jet Workshop – Paris June 20, 2008

35. Particle Flow: basic idea Associate hits with each sub-detector Jet Workshop – Paris June 20, 2008

36. Particle Flow: basic idea Make links across sub-detectors Jet Workshop – Paris June 20, 2008

37. Particle Flow: basic idea Apply particle identification and separate the particles Jet Workshop – Paris June 20, 2008