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Study of jets and their properties in PbPb collisions using the ATLAS detector at LHC

Study of jets and their properties in PbPb collisions using the ATLAS detector at LHC. Martin Spousta Ústav částicové a jaderné fyziky, MFF UK spousta @ipnp.troja.mff.cuni.cz. Outline. Basic information about ATLAS HI program Atlas Calorimetry & PbPb jet algorithms

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Study of jets and their properties in PbPb collisions using the ATLAS detector at LHC

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  1. Study of jets and their properties in PbPb collisions using the ATLAS detector at LHC Martin SpoustaÚstav částicové a jaderné fyziky,MFF UKspousta@ipnp.troja.mff.cuni.cz

  2. Outline • Basic information about ATLAS HI program • Atlas Calorimetry & PbPb jet algorithms • Atlas Tracking & PbPb jet algorithms

  3. ATLAS Heavy Ion working group M. Baker, R. Debbe, A. Moraes, R. Nouicer, P. Steinberg, H. Takai, F. Videbaek, S. WhiteBrookhaven National Laboratory, USA J. Dolejsi, M. SpoustaCharles University, Prague A. Angerami, B. Cole, N. Grau, W. Holzmann, M. LelchoukColumbia Unversity, Nevis Laboratories, USA § § § § §  L. RosseletUniversity of Geneva, Switzerland  §  A. DenisovIHEP, Russia A. Olszewski, B. Toczek, A. Trzupek, B. Wosiek, K. WozniakIFJ PAN, Krakow, Poland J. Hill, A. Lebedev, M. RosatiIowa State University, USA V. PozdnyakovJINR, Dubna, Russia S. TimoshenkoMePHI, Moscow, Russia Jet subgroup: Aaron Angerami, Brian Cole, Jiří Dolejší, Nathan Grau, Wolf Holzmann, Martin Spousta P. Chung, J. Jia, R. Lacey, N N.. AjitanandChemistry Department, Stony Brook University, USA G. Atoian, V. Issakov, H. Kasper, A. Poblaguev, M. ZellerYale University, USA

  4. ATLAS detector, LHC compared to RHIC • Current status: • first cosmic data taking started last year • first beam expected at July this year

  5. ATLAS calorimetry in progress in progress • Advantages of the calorimeter • full f-range coverage • large coverage in h (|hmax| = 5.0) • fine longitudinal segmentation and granularity (first layers of EMCal 0.003 x 0.1) • Calorimeter is well suited for the jet reconstruction and gamma isolation

  6. Presampler Barrel em. The whole calorimeter 4.5 1.2 2.5 End-cap em. Tilecal 3 0.9 Average Hijing background (around 3 GeV) Population of different layers (100 events) LAr end-cap Forward calorimeter 0.9 0.3

  7. 50% Presampler Barrel em. The whole calorimeter 100% 1.6 30 25 30% 15% Endcap em. Tilecal 8 5 Pythia pp-jets Population of different layers (an example) LAr end-cap Forward calorimeter 0.02 0.4 process: qq → WH(120) → m nmuu

  8. … at LHC energies it is possible to measure „real“ jets, but … = + Total jet cross section coupling constant of QCD … but how to deal with the background? -- New algorithms needed ? = “merged event” = PYTHIA event merged with HIJING event at the generator level, then reconstructed reconstruction of PYTHA event

  9. HIJING HYDJET Pythia pp AA Cone 0.4, 0.7 Calo Towers (0.1 x 0.1) Tower Noise suppression Jet Finding kT Calo Jets (not calibrated) Tower Building Jet Calibration (e/h, inactive mat.) Calo Cells Calo Jets (calibrated) Physics Jets In-situ Calibration (underlying event, physics environment) Jet ET Scale Corrections (noise, pile up, algorithm effects) Physics Jets Generation Suitable algorithms Herwig HepMC ATLAS-CSC-01-02-00 Rome-Final ATLAS-DC3-07 Simulation G4 Hits Digitization Real Data G4 Digits Reconstruction ESD CBNT AAN SAN Analysis

  10. Clone Cells Calibrate Cells Read Events – Averaged Bkgr from Files Find Seeds: FixedThreshold Find Seeds: SlidingWindow Read Events – Averaged Bkgr from Files Perform Cell Bkgr Subtraction Perform Tower Bkgr Subtraction implemented in HIJetRec package Build Towers EventsAvgCellBkgrSubtr AvgCellBkgrSubtr EventsAvgTowBkgrSubtr SetCellBkgrSubtr AvgTowBkgrSubtr Build Towers Build “ProtoJets”, Run Cone Algorithm CryoCorr or JetScale or both or none http://atlas-sw.cern.ch/cgi-bin/viewcvs-atlas.cgi/offline/Reconstuction/HeavyIonRec/HIJetRec Calibrate Jets Apply Cuts, Receive Jets

  11. One Pythia event … different jet energy bins are used: “J2” jets – 35-70 GeV (initial parton), “J3” jets – 70-140 GeV, “J4” jets – 140-280 GeV

  12. One “merged” event … different centrality bins are used (b=2,4,6,8,10), here b=2 (dN/dh ~ 2700)

  13. Merged after “Cell background subtraction”

  14. Jet position resolution AvgCellBkgrSubtrSetCellBkgrSubtr AvgCellBkgrSubtrSetCellBkgrSubtr RMSDf 400 events, J3 jets, b=2 • 3x1300 events, for one jet bin, • J2-J4 jets • different centralities

  15. Jet energy resolution 3 different background strategies, but very similar results energy resolution slightly dependent on the longitudinal structure of the calorimeter • SetCellBkgrSubtr I.◦ AvgCellBkgrSubtr • SetCellBkgrSubtr II.◦ AvgCellBkgrSubtr

  16. Jet energy scale • Different reasons of the under-subtraction: • Lose efficiency at energies bellow 60 GeV  we are effectively picking up only upwards fluctuations (good correlation between efficiency and jet scale shift for different setups) • HIJING “mini-jets” (correlation between the sum of Pt of HIJING particles (from the jet area) and the shift in the jet energy) • Contribution from fake jets • Some external technical problems with truth jet reconstruction … 2007 results strange “under-subtraction” small eta-dependent jet energy scale shift still present Now

  17. Fake rate / Efficiency dN/d = 2700 • 40% fake rate at 40 GeV, 30% efficiency at 60 GeV (for b=2) • different methods how to deal with fakes under the investigation (general idea: fake jets have very different shape comparing to real jets => different “jet-rejector-variables”)

  18. Jet shapes –pp(PYTHIA) Open – truth jets and particles Closed – reco jets and cells Circles – reco jets and constituents Squares – reco jets and cells Black – truth jets and particles Red – reco jets and cells “J3” jets (70 GeV – 140 GeV), 400 events

  19. Jet shapes – reconstruction in PbPb compared with pp Open – pythia jets and cells Closed – merged jets and cells Open – pythia jets and constituents Closed – merged jets and constituents Black – pythia jets and cells Red – merged jets and cells Black – pythia jets and constituents Red – merged jets and constituents Two strategies – to use cells or constituents (i.e. here towers)

  20. Other suitable observables … radial moments pp PbPb R=0.1 … difference between u-quark jets(full line) and b-quark jets (dashed line) is observable R=0.2 R=0.3 (old results from f77 simulations with ATLSIM,few hundreds of events) R=0.4

  21. ATLAS tracking Occupancy at dN/dh ~ 3000 Inner detector - eta coverage is 2.5 Silicon pixel detector – 3 layers, (RF = 12 mm, z = 66 mm) … Semiconductor tracker (SCT), (RF = 16 mm, z = 580 mm) … Transition radiation tracker (TRT), (170 mm / straw) …

  22. Tracking efficiency and resolutions Sum of Pt (of tracks within a jet) vs Et (of jet) track  jet association works well … tracking efficiency ~70% for dN/dh~2700

  23. Track to calo matching, shape of the jet from tracking Pt,min(track) = 4 GeV Pt,min(track) = 4 GeV Pt,min(track) = 1 GeV Pt,min(track) = 1 GeV Dh, Df between a track and jet axis (400 Pythia J3 events)

  24. Track to Calo matching, jT and z distributions in pp • jT is transverse moment of a constituent of jet with respect to the jet axis • z is a fraction of longitudinal moment of a constituent with respect to the jet axis • open = truth (Pythia particles within a jet and & Truth jets) • full = reconstruction of Pythia event (tracks that match calorimeter towers of a jet) (i.e. proton – proton) • no tracking efficiency correction

  25. Track to Calo matching, jT and z distributions in PbPb RMS(reco) = 0.482 ± 0.003 RMS(truth) = 0.398 ± 0.003 RMS(reco) = 0.506 ± 0.003 RMS(reco) = 0.075 ± 0.0005 RMS(truth) = 0.079 ± 0.0005 RMS(reco) = 0.079 ± 0.0005 tracking efficiency correction (~70%) tracking efficiency correction (~70%) +truth jet axis instead of reconstructed

  26. Conclusion • New algorithms for background subtraction and jet reconstruction has been developed and implemented in the official reconstruction software of the ATLAS detector • Several important quantities are proposed to measure in HI collisions at ATLAS (namely jet shapes, radial moments, jT and z distributions) • Performance of the reconstruction was/is tested • New algorithms are still developed (fast-kT, fake rejections, …), existing algorithms are tuned to stay actual within the framework of evolving reconstruction software • Jet reconstruction in HI is very close to be prepared for data taking => now, it is the best time to join us 

  27. ATLAS Heavy Ion working group M. Baker, R. Debbe, A. Moraes, R. Nouicer, P. Steinberg, H. Takai, F. Videbaek, S. WhiteBrookhaven National Laboratory, USA J. Dolejsi, M. SpoustaCharles University, Prague A. Angerami, B. Cole, N. Grau, W. Holzmann, M. LelchoukColumbia Unversity, Nevis Laboratories, USA § § § § §  L. RosseletUniversity of Geneva, Switzerland  §  A.DenisovIHEP, Russia A. Olszewski, B. Toczek, A. Trzupek, B. Wosiek, K. WozniakIFJ PAN, Krakow, Poland J. Hill, A. Lebedev, M. RosatiIowa State University, USA V. PozdnyakovJINR, Dubna, Russia S.TimoshenkoMePHI, Moscow, Russia Jet subgroup: Aaron Angerami, Brian Cole, Jiří Dolejší, Nathan Grau, Wolf Holzmann, Martin Spousta P.Chung, J. Jia, R. Lacey, N N.. AjitanandChemistry Department, Stony Brook University, USA G. Atoian, V. Issakov, H. Kasper, A. Poblaguev, M. ZellerYale University, USA

  28. Založní snímky

  29. LHC energie Phenomenology QCD C.A.Salgado, U.Wiedemann Experiment

  30. Heavy Ions at the ATLAS detector, few numbers Calorimeter Inner detector

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