New STAR jet measurements and their implications for EMCal trigger and offline Peter Jacobs, LBNL EMCAl Meeting Nantes, July Jet probes of the Jet in STAR and EMCal 1

EMCAl Meeting Nantes, July Jet probes of the Jet in STAR and EMCal 2 This talk is almost entirely about recent analysis of STAR data on full jet reconstruction in central Au+Au data based on Hard Probes talks by Sevil Salur and Joern Putschke plus additional material from Mateusz Ploskon Important lessons for ALICE EMCal jet program: trigger and offline

STAR jet spectrum 3 STAR: published inclusive jet for √s=200 GeV p+p has statistical reach beyond 40 GeV: based on 0.2 pb -1. STAR 2007 Au+Au 200 GeV dataset ~ 5 pb -1 “p+p equivalent”: jets must be there!! Phys. Rev. Lett. 97 (2006) Systematic uncertainty ~50% due to trigger xsections Unbiased jet reconstruction in AuAu: cross section must scale as N binary relative to p+p

Jet reconstruction algorithms EMCAl Meeting Nantes, July Jet probes of the Jet in STAR and EMCal 4

5 Jets and the presence of underlying event  = Diffuse noise,  = noise fluctuations STAR Preliminary Au+Au Central   Fact from the data: 200 GeV central Au+Au: R=0.4 → Bkg Energy ~ 40 GeV Backgrounds estimated event by event. Cone: Look at out-of jet cones A=πR 2 Sequential Recombination: Estimate the active area of each jet by addition of zero energy particles of known density. M. Cacciari, G. Salam, G. Soyez [hep-ph]

MP, 18th of June 2008 UC Davis 6 Resolution and backgrounds: Pythia jets embedded in real Au+Au events LOHSC KTCAMB p T cut =1 GeV R=0.4 Seed=4.6 GeV Counts ∆E Event by event comparison of PyTrue vs PyDet vs PyEmbed. p T cut =1 GeV R=0.4 STAR Preliminary E T =35±5 GeV  E = E PyDet – E pyTrue Shift of median due to un-measured particles (n, K 0 L ) and the p T cut.  E = E PyEmbed – E pyDet Smearing due to background subtraction in Au+Au.  E = E PyEmbed - E PyTrue Tail at positive ∆E causes a kick in the spectrum.

MP, 18th of June 2008 UC Davis 7 Jet spectrum: effects of energy resolution and p T cut Increase p T threshold: Reduce the effect of background fluctuations jet reconstruction in 0-10% Au+Au is similar in p+p However, the p T cut introduces (strong) biases… Similar effects observed for K T & Cambridge/Aachen p T cut =0.1 GeV dN Jet /dE T (per event)‏ LOHSC seed=4.6 GeV R=0.4 PyDet PyEmbed PyTrue PyDet PyEmbed PyTrue PyDet PyEmbed PyTrue p T cut =1.0 GeV LOHSC seed=4.6 GeV R=0.4 LOHSC seed=4.6 GeV R=0.4 E T [GeV] STAR Preliminary p T cut =2.0 GeV

MP, 18th of June 2008 UC Davis 8 Jet areas and background fluctuations Au+Au bkgd: reduction in area & increase in fluctuations Pythia jets embedded in real Au+Au bkgd: same area and fluctuations as real Au+Au jets Cacciari+Salam: understood analytically – prefer anti-kT and SISCone algorithms Counts MB-trig PyEmbed PyTrue R=0.4 KT p T cut = 0.1 GeV Jet Area Counts STAR Preliminary Jet E T > 20 GeV Au+Au 0-10% Background Fluctuations R=0.4 KT p T cut = 0.1 GeV MB-trig PyEmbed PyTrue Sigma STAR Preliminary Au+Au 0-10% M. Cacciari, G. Salam, G. Soyez [hep-ph] Jet Area

9 Cross section compared to binary-scaled p+p Two Au+Au online trigger conditions: MB-Trig: minimum-bias, offline centrality selection (no EMC tower cuts) HT-Trig: E T >7.5 GeV in 3x3 EMC tower cluster Inclusive spectrum correction based on PYTHIA E T [GeV] dN Jet /dE T (per event)‏ N bin scaled p+p Au+Au 0-10%  MB-Trig O HT-Trig R=0.4 p T cut =1 GeV Seed=4.6 GeV LOHSC Statistical Errors Only MB-Trig: Good agreement (within errors) with binary-scaled p+p → unbiased jet reconstruction? HT-Trig: large trigger bias persists beyond 30 GeV → use with caution! Conclusion: MB-Trig is essential for unbiased measurement good news: factor ~20 more on tape than shown here! (QM2009…)

10 Scaling for sequential recomb. w/ low p T cut Also good agreement with binary-scaled p+p! Interesting because: seedless algorithms: no seed bias pT>100 MeV (!): minimal p T cut bias spectrum correction factors (much) closer to unity E T [GeV] dN Jet /dE T (per event)‏ E T [GeV] dN Jet /dE T (per event)‏ Au+Au 0-10% STAR Preliminary R=0.4 p T cut =0.1 GeV KT Statistical Errors Only Au+Au 0-10% STAR Preliminary R=0.4 p T cut =0.1 GeV CAMB Statistical Errors Only N bin scaled p+p  MB-Trig O HT-Trig  MB-Trig O HT-Trig

11 Imprecise subtraction of underlying event? How sensitive are we to fragmentation model in corrections (PYTHIA) ? Systematics of p T -cut bias 11 STAR Preliminary P T Cut KT LOHSC Au+Au 0-10%  MB-Trig  N bin Scaled p+p LOHSC KT STAR Preliminary Au+Au 0-10% Au+Au 0-10%  MB-Trig  N bin Scaled p+p Au+Au 0-10%  MB-Trig  N bin Scaled p+p STAR Preliminary Au+Au 0-10%  MB-Trig  N bin Scaled p+p Au+Au 0-10%  MB-Trig  N bin Scaled p+p Au+Au 0-10%  MB-Trig  N bin Scaled p+p

MP, 18th of June 2008 UC Davis 12 Next step: medium modification of the fragmentation function

13 FF for PYTHIA jet in centralAu+Au bkgd

14 FF for Central Au+Au (data) Jet E T > 30 GeV

15 FF: compare p+p to central Au+Au (HT-trig) ξ not corrected for E T shift due to quenching need quenching model + “data-driven” cross checks (gamma+jet, dijet) Provisional conclusion: apparently no medium-induced FF modification! but consistent with HT-trig being highly biased (require 7.5 GeV  0 in jet) Factor 20 more MB-trig data on tape – crucial to analyse it!

Dijets in central Au+Au data EMCAl Meeting Nantes, July Jet probes of the Jet in STAR and EMCal 16 From HT-trig sample

Dijet azimuthal and energy balance: p+p vs central Au+Au EMCAl Meeting Nantes, July Jet probes of the Jet in STAR and EMCal 17 Clear di-jet signal for E T > 20 GeV utilize E T -balance for model-independent measurement of resolution? → already in progress for STAR p+p data acoplanarity may be the most fundamental jet quenching measurement

MP, 19th of June 2008 DAVIS 18 Theory remarks U. Wiedemann, HP08

New jet observable: subjet distributions EMCAl Meeting Nantes, July Jet probes of the Jet in STAR and EMCal 19 Look at multiplicity of “subjets” as fn of Durham/Cambridge metric: K. Zapp, U. Wiedemann et al, arXiv: medium-enhanced jet splitting

Lessons from STAR jet measurements for ALICE EMCal 20 The (very) good news: significant theory progress in reconstruction algorithms (Salam, Cacciari, et al.), driven largely by high lumi p+p but of great utility for heavy ions jet reconstruction over heavy ion backgrounds appears to work well…much better than expected? But strong biases evident due to Cluster triggers Seeded jet reco algorithms p T cuts on tracks contributing to jets → need to be careful with triggers and reco algorithms – you will always get what you ask for but perhaps not what you want

Lessons for EMCal II 21 EMCal jet trigger: large patch clearly essential revisit with modern quenching MCs (JEWEL,…) tuned on STAR data (available Sept ’08?) Offline jet reco: Standard seeded algorithm is sub-optimal unseeded algorithms clearly prefered: FastJet package (anti- kT, SISCone are optimal) Crucial to develop “data-driven” calibrations of jet reco in heavy ion collisions:  +jet Revisit Z+jet? 100 central Pb+Pb events already useful Utilize geometric bias: hard  0 +jet? ….

Lessons for EMCal III 22 Medium-induced modification of Fragmentation Function is a tricky measurement has been useful benchmark but perhaps not the first generation of jet quenching measurements? how good is theoretical control? → consider new, theoretically well-controlled observables, e.g. subjet distributions (well studied at LEP)

EMCAl Meeting Nantes, July Jet probes of the Jet in STAR and EMCal23 Extra slides

24 One of the critical issues: background fluctuations – LOHS Cone

EMCAl Meeting Nantes, July Jet probes of the Jet in STAR and EMCal25

EMCAl Meeting Nantes, July Jet probes of the Jet in STAR and EMCal26

EMCAl Meeting Nantes, July Jet probes of the Jet in STAR and EMCal27

EMCAl Meeting Nantes, July Jet probes of the Jet in STAR and EMCal28

EMCAl Meeting Nantes, July Jet probes of the Jet in STAR and EMCal29