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Mateusz Ploskon For the STAR Collaboration

Inclusive jet cross-sections and correlations in Au+Au and p+p collisions at sqrt ( s NN ) = 200 GeV. Mateusz Ploskon For the STAR Collaboration. Outline. Motivation and strategy Datasets, jet algorithms, correction schemes Observables Inclusive jet cross-section + Jet R AA

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Mateusz Ploskon For the STAR Collaboration

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  1. Inclusive jet cross-sections and correlations in Au+Au and p+p collisions at sqrt(sNN) = 200 GeV Mateusz Ploskon For the STAR Collaboration

  2. Outline • Motivation and strategy • Datasets, jet algorithms, correction schemes • Observables • Inclusive jet cross-section + Jet RAA • Jet “radius” systematics • (di-)Hadron-jet recoil spectrum and Jet IAA • Discussion: implications for jet quenching Mateusz Ploskon (LBNL), STAR, QM'09

  3. Motivation and Strategy R Physics of full jet reconstruction in heavy ion collisions Jet R=0.4 Measure energy flow into “cone” of radius R Energy shift? p+p Absorption? p0 Au+Au • Total momentum is conserved even for strongly quenched jets • Unbiased jet reconstruction: recover the full jet energy within • cone radius R • Compare Au+Au and p+p jet spectra • → Inclusive cross section: RAAjet~1 • → p0+jet conditional yield: IAAjet~1 • Caveat: initial state nuclear effects Cross-section ratio AuAu/pp 1 Mateusz Ploskon (LBNL), STAR, QM'09

  4. Data sets Essential requirement: minimize trigger bias of jet population • Inclusive spectrum data: • Au+Au (2007): • online MinBias Trigger • offline select 10% most central events (8 x106events) • p+p (2006): • online “Jet Patch” trigger (intlumi 6.5 pb^-1) • offline correct bias at low ET • h+jet coincidence data: • Both p+p (2006) and Au+Au (2007): • Online “BEMC High Tower” trigger • Offline: hadron trigger energy from 3x3 tower cluster (ET>7 GeV) • Track and Tower cuts: • BEMC towers energy > 0.2 GeV • TPC track momentum pT > 0.2 GeV/c Mateusz Ploskon (LBNL), STAR, QM'09

  5. KT jet anti-kTjet Jet algorithms Anti-kt expected to be less susceptible to background effects in heavy ion collisions Algorithms: kt and anti-kt from FastJet* • Resolution parameter R = 0.4, 0.2 • Jet acceptance: |hJET|< 1.-R • Recombination scheme: E-scheme with massless particles R Sequential recombination algorithms Cone based algorithms Fragmentation Hard scattering *Cacciari, Salam and Soyez, JHEP 0804 (2008) 005 [arXiv:0802.1188] Mateusz Ploskon (LBNL), STAR, QM'09

  6. Systematic corrections Trigger corrections • p+p trigger bias correction Particle level corrections: • Detector effects: efficiency and pT resolution • “Double* counting” of particle energies • * electrons: - double; hadrons: - showering corrections • All towers matched to primary tracks are removed from the analysis Jet level corrections: • Spectrum shift: • Unobserved energy • TPC tracking efficiency • BEMC calibration (dominant uncertainty in p+p) • Jet patch trigger efficiency (only in p+p) • Jet pT resolution • Underlying event (dominant uncertainty in Au+Au) Full assessment of jet energy scale uncertainties Data driven correction scheme • Weak model dependence: only for single-particle response, p+p trigger response • No dependence on quenching models Mateusz Ploskon (LBNL), STAR, QM'09

  7. Heavy Ions and background characterization Mateusz Ploskon (LBNL), STAR, QM'09

  8. STAR Preliminary Au+Au Central   Underlying event Single di-jet event from a central Au+Au: - Two jet peaks on top of the HI background Central assumption: Signal and background can be factorized Event background is characterized with median pT per unit area (r). <pTBG> in R = 0.4 is~45 GeV/c. S/B~0.5 at 20 GeV/c. Systematic studies indicate variation of sigma +/-1 is a conservative bracketing of systematic uncertainties. Error bands indicate these limits. with resolution: True jet distribution smeared: sDATA ~ 6.8 GeV Mateusz Ploskon (LBNL), STAR, QM'09

  9. Fake jet contamination “Fake” jets: signal in excess of background model from random association of uncorrelated soft particles (i.e. not due to hard scattering) “Fake” jet rate estimation: • Central Au+Au dataset (real data) • Randomize azimuth of each charged particle and calorimeter tower • Run jet finder • Remove leading particle from each found jet • Re-run jet finder STAR Preliminary Mateusz Ploskon (LBNL), STAR, QM'09

  10. Fake jet contamination “Fake” jets: signal in excess of background model from random association of uncorrelated soft particles (i.e. not due to hard scattering) “Fake” jet rate estimation: • Central Au+Au dataset (real data) • Randomize azimuth of each charged particle and calorimeter tower • Run jet finder • Remove leading particle from each found jet • Re-run jet finder STAR Preliminary Mateusz Ploskon (LBNL), STAR, QM'09

  11. Spectrum unfolding: method • Background non-uniformity (fluctuations) and energy resolution introduce pT-smearing • Correct via “unfolding”: inversion of full bin-migration matrix • Check numerical stability of procedure using jet spectrum shape from PYTHIA Pythia Pythia smeared Pythia unfolded unfolding • Procedure is numerically stable • Correction depends critically on background model • → main systematic uncertainty for Au+Au Mateusz Ploskon (LBNL), STAR, QM'09

  12. Spectrum unfolding • Corrections for smearing of jet pT due to HI backround non-uniformities • 1) raw spectrum STAR Preliminary Mateusz Ploskon (LBNL), STAR, QM'09

  13. Spectrum unfolding • Corrections for smearing of jet pT due to HI backround non-uniformities • 1) raw spectrum • 2) removal of “fake”-correlations STAR Preliminary Mateusz Ploskon (LBNL), STAR, QM'09

  14. Spectrum unfolding • Corrections for smearing of jet pT due to HI backround non-uniformities • 1) raw spectrum • 2) removal of “fake”-correlations • 3) unfolding STAR Preliminary Mateusz Ploskon (LBNL), STAR, QM'09

  15. Spectrum unfolding • Corrections for smearing of jet pT due to HI backround non-uniformities • 1) raw spectrum • 2) removal of “fake”-correlations • 3) unfolding • 4) correction for pT resolution STAR Preliminary Mateusz Ploskon (LBNL), STAR, QM'09

  16. Jet yields in p+pat sqrt(sNN) = 200 GeV Mateusz Ploskon (LBNL), STAR, QM'09

  17. Inclusive jet cross-section in p+p at sqrt(sNN) = 200 GeV • Fully corrected jet cross-section reconstructed with kt algorithm • Very good agreement between the algorithms STAR Preliminary Uncertainty due to BEMC calibration Mateusz Ploskon (LBNL), STAR, QM'09

  18. Inclusive jet cross-section in p+p at sqrt(sNN) = 200 GeV • Comparison to published STAR data • run 2003/2004 • Note: • published data reconstructed with different jet algorithm: • Mid-point cone (R=0.4) STAR Preliminary Phys. Rev. Lett. 97 (2006) 252001 Mateusz Ploskon (LBNL), STAR, QM'09

  19. Inclusive jet yields in 10% most central Au+Au at sqrt(sNN) = 200 GeV Mateusz Ploskon (LBNL), STAR, QM'09

  20. Inclusive jet yields in 10% most central Au+Au at sqrt(sNN) = 200 GeV • Fully corrected jet spectrum • Exactly the same algorithms and jet definitions used as compared to p+p • Bands on data points represent estimation of systematic uncertainties due to background subtraction STAR Preliminary Uncertainty due to BEMC calibration Mateusz Ploskon (LBNL), STAR, QM'09

  21. Inclusive jet spectrum: p+p and central Au+Au (R=0.4 and R=0.2) p+p Au+Au central STAR Preliminary STAR Preliminary Mateusz Ploskon (LBNL), STAR, QM'09

  22. Cross-section ratios in p+p and Au+ Au with R=0.2/R=0.4 Many systematics effects cancel in the ratio p+p Au+Au STAR Preliminary p+p: “Narrowing” of the jet structure with increasing jet energy Au+Au: Strong broadening of the jet energy profile Mateusz Ploskon (LBNL), STAR, QM'09

  23. Nuclear modification factor for jets Mateusz Ploskon (LBNL), STAR, QM'09

  24. RAA Jets • Significant energy recovered as compared to RAA~0.2 for hadrons • Visible trends: • different sensitivity of the algorithms • Central values drop as a function of jet pT R = 0.4 STAR Preliminary 5% uncertainty on BEMC calibration Mateusz Ploskon (LBNL), STAR, QM'09

  25. RAA Jets and Energy flow in smaller “cone” radii R=0.4 STAR Preliminary R=0.2 Significant drop of RAA as a function of jet pT for R=0.2 as compared to R=0.4 Jet energy not fully recovered in small “cones” – shift towards lower pT Mateusz Ploskon (LBNL), STAR, QM'09

  26. Hadron-jet coincidences Mateusz Ploskon (LBNL), STAR, QM'09

  27. STAR, PRL 97, 162301 (2006) Yield per trigger Df Di-hadron – jet correlations A STAR high pTdihadrons: bias towards non-interacting jet population Recoil B Mateusz Ploskon (LBNL), STAR, QM'09

  28. Hadron+jet coincidence Energy shift? R=0.4 p+p Conditional yield Absorption? Au+Au p0 • Trigger on hard, leading p0 (pT>6 GeV/c) • 3x3 tower cluster in BEMC • Construct spectrum of recoil jets • normalized per di-hadron trigger • This event selection will maximize • the recoil path length distribution • in matter R Jet Cond. yield ratio AuAu/pp 1 Mateusz Ploskon (LBNL), STAR, QM'09

  29. H – recoil jet coincidences A A Recoil jet Recoil jet B pT> 0.5 GeV/c B pT> 6 GeV/c Use jet fragmentation bias to vary jet path length distribution? Mateusz Ploskon (LBNL), STAR, QM'09

  30. H – recoil jet coincidences STAR Preliminary A (Au+Au: 10% central) Anti-kt R=0.4 B Mateusz Ploskon (LBNL), STAR, QM'09

  31. H – recoil jet coincidences STAR Preliminary A (Au+Au: 10% central) Anti-kt R=0.4 B Mateusz Ploskon (LBNL), STAR, QM'09

  32. H – recoil jet coincidences STAR Preliminary A (Au+Au: 10% central) Anti-kt R=0.4 B Mateusz Ploskon (LBNL), STAR, QM'09

  33. H – recoil jet coincidences STAR Preliminary A (Au+Au: 10% central) Anti-kt R=0.4 B Significant suppression of the bias free recoil jet spectrum Mateusz Ploskon (LBNL), STAR, QM'09

  34. Summary Au+Au 10% central STAR Preliminary • Qualitatively new measurement of jet quenching in terms of energy flow (rather than hadronic observables) has been established • What we have shown: • Minimum bias dataset: RAA~0.5 or larger • Significant broadening of jet energy profile R=0.2 -> R=0.4 • Strong suppression of recoil jet rate at maximum path-length • Consistent interpretation: • quenching induces jet broadening • R=0.4 (with the presented jet definitions) is insufficient for unbiased reconstruction RAu+Au 10% central STAR Preliminary STAR Preliminary STAR Preliminary Mateusz Ploskon (LBNL), STAR, QM'09

  35. Outlook • Rich new set of observables to confront calculations • New MC jet quenching models (qPythia, JEWEL, T. Renk) • New jet algorithms for HI collisions? • Post-processing – integration of significant energy flow outside the “initial” jet areas (however probably hard to calculate/resolve theoretically Mateusz Ploskon (LBNL), STAR, QM'09

  36. Extra slides Mateusz Ploskon (LBNL), STAR, QM'09

  37. Spectrum unfolding- correction for smearing of jet pt due to bgnonuniformities R=0.2 R=0.4 Mateusz Ploskon (LBNL), STAR, QM'09

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