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New STAR jet measurements and their implications for EMCal trigger and offline. Peter Jacobs, LBNL. This talk is almost entirely about recent analysis of STAR data on full jet reconstruction in central Au+Au data
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New STAR jet measurements and their implications for EMCal trigger and offline Peter Jacobs, LBNL Jet probes of the Jet in STAR and EMCal
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 SevilSalur and JoernPutschke plus additional material from Mateusz Ploskon • Important lessons for ALICE EMCal jet program: trigger and offline Jet probes of the Jet in STAR and EMCal
STAR jet spectrum Phys. Rev. Lett. 97 (2006) 252001 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!! Unbiased jet reconstruction in AuAu: cross section must scale as Nbinaryrelative to p+p Systematic uncertainty ~50% due to trigger xsections
Jet reconstruction algorithms Jet probes of the Jet in STAR and EMCal
Jets and the presence of underlying event • Fact from the data: • 200 GeV central Au+Au: R=0.4 → Bkg Energy ~ 40 GeV • Backgrounds estimated event by event. • Cone: Look at <pT> out-of jet cones A=πR2 • Sequential Recombination: Estimate the • active area of each jet by addition of zero energy • particles of known density. • M. Cacciari, G. Salam, G. Soyez 0802.1188 [hep-ph] STAR Preliminary Au+Au Central = Diffuse noise, = noise fluctuations
Resolution and backgrounds: Pythia jets embedded in real Au+Au events Event by event comparison of PyTruevsPyDetvsPyEmbed. Seed=4.6 GeV pTcut =1 GeV R=0.4 KT pTcut =1 GeV R=0.4 CAMB LOHSC pTcut =1 GeV R=0.4 Counts ET=35±5 GeV ET=35±5 GeV ET=35±5 GeV STAR Preliminary STAR Preliminary STAR Preliminary ∆E ∆E ∆E E = EPyDet – EpyTrue Shift of median due to un-measured particles (n, K0L) and the pT cut. E = EPyEmbed – EpyDet Smearing due to background subtraction in Au+Au. E = EPyEmbed - EPyTrue Tail at positive ∆E causes a kick in the spectrum. STAR @ UC Davis
Jet spectrum: effects of energy resolution and pT cut pTcut=0.1 GeV pTcut=1.0 GeV pTcut=2.0 GeV seed=4.6 GeV seed=4.6 GeV seed=4.6 GeV R=0.4 R=0.4 R=0.4 LOHSC LOHSC LOHSC dNJet/dET (per event) PyDet PyEmbed PyTrue PyDet PyEmbed PyTrue PyDet PyEmbed PyTrue STAR Preliminary STAR Preliminary STAR Preliminary ET [GeV] Increase pT threshold: Reduce the effect of background fluctuations jet reconstruction in 0-10% Au+Au is similar in p+p However, the pT cut introduces (strong) biases… Similar effects observed for KT & Cambridge/Aachen STAR @ UC Davis
Jet areas and background fluctuations M. Cacciari, G. Salam, G. Soyez 0802.1188 [hep-ph] Jet Area Background Fluctuations Au+Au 0-10% Au+Au 0-10% Counts MB-trig PyEmbed PyTrue MB-trig PyEmbed PyTrue Counts Jet ET> 20 GeV pT cut = 0.1 GeV KT KT R=0.4 R=0.4 pTcut = 0.1 GeV STAR Preliminary STAR Preliminary Jet Area Sigma • Au+Aubkgd: reduction in area & increase in fluctuations • Pythiajets embedded in real Au+Aubkgd: same area and fluctuations as real Au+Aujets • Cacciari+Salam: understood analytically – prefer anti-kT and SISCone algorithms STAR @ UC Davis
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: ET>7.5 GeV in 3x3 EMC tower cluster • Inclusive spectrum correction based on PYTHIA Nbin scaled p+p Au+Au 0-10% MB-Trig O HT-Trig dNJet/dET (per event) LOHSC R=0.4 pT cut =1 GeV Seed=4.6 GeV 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…) ET [GeV]
Scaling for sequential recomb. w/ low pT cut STAR Preliminary STAR Preliminary Nbin scaled p+p Nbin scaled p+p Au+Au 0-10% Au+Au 0-10% MB-Trig O HT-Trig MB-Trig O HT-Trig dNJet/dET (per event) dNJet/dET (per event) KT CAMB R=0.4 pT cut =0.1 GeV R=0.4 pT cut =0.1 GeV Statistical Errors Only Statistical Errors Only ET [GeV] ET [GeV] • Also good agreement with binary-scaled p+p! • Interesting because: • seedless algorithms: no seed bias • pT>100 MeV (!): minimal pTcut bias • spectrum correction factors (much) closer to unity
Systematics of pT-cut bias Au+Au 0-10%MB-Trig Nbin Scaled p+p Au+Au 0-10%MB-Trig Nbin Scaled p+p Au+Au 0-10%MB-Trig Nbin Scaled p+p STAR Preliminary LOHSC LOHSC LOHSC STAR Preliminary STAR Preliminary STAR Preliminary Au+Au 0-10% Au+Au 0-10%MB-Trig Nbin Scaled p+p Au+Au 0-10%MB-Trig Nbin Scaled p+p Au+Au 0-10%MB-Trig Nbin Scaled p+p STAR Preliminary STAR Preliminary STAR Preliminary KT KT KT PT Cut 11 Imprecise subtraction of underlying event? How sensitive are we to fragmentation model in corrections (PYTHIA) ?
Next step: medium modification of the fragmentation function STAR @ UC Davis
FF: compare p+p to central Au+Au (HT-trig) • ξnot corrected for ET 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 GeVp0 in jet) • Factor 20 more MB-trig data on tape – crucial to analyse it!
Dijets in central Au+Au data From HT-trig sample Jet probes of the Jet in STAR and EMCal
Dijet azimuthal and energy balance: p+p vs central Au+Au • Clear di-jet signal for ET> 20 GeV • utilize ET-balance for model-independent measurement of resolution? → already in progress for STAR p+p data • acoplanarity may be the most fundamental jet quenching measurement Jet probes of the Jet in STAR and EMCal
Theory remarks U. Wiedemann, HP08 STAR @ DAVIS
New jet observable: subjet distributions Look at multiplicity of “subjets” as fn of Durham/Cambridge metric: K. Zapp, U. Wiedemann et al, arXiv:0804.3568 medium-enhanced jet splitting Jet probes of the Jet in STAR and EMCal
Lessons from STAR jet measurements for ALICE EMCal • 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 • pT 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 • 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: • g+jet • Revisit Z+jet? 100 central Pb+Pb events already useful • Utilize geometric bias: hard p0+jet? • ….
Lessons for EMCal III • 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)
Extra slides Jet probes of the Jet in STAR and EMCal
One of the critical issues: background fluctuations – LOHS Cone