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Studies of the jet fragmentation in p+p collisions in STAR

Studies of the jet fragmentation in p+p collisions in STAR. Elena Bruna Yale University. STAR Collaboration meeting, June 16-21 2008. OUTLINE. Jets in p+p at STAR Jet reconstruction: Jet Finding Algorithm Theoretical and Experimental Issues in Jet Finding Performance

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Studies of the jet fragmentation in p+p collisions in STAR

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  1. Studies of the jet fragmentation in p+p collisions in STAR Elena Bruna Yale University STAR Collaboration meeting, June 16-21 2008

  2. OUTLINE • Jets in p+p at STAR • Jet reconstruction: • Jet Finding Algorithm • Theoretical and Experimental Issues in Jet Finding • Performance • Fragmentation functions on p+p events • Conclusions Elena Bruna, Yale University

  3. HIGH-pT AT RHIC p+p collisions Elena Bruna, Yale University

  4. JETS IN p+p COLLISIONS • Hard probes  early times • Calculable in pQCD: factorization theorem hadrons D Jet cross section: c, xc Elena Bruna, Yale University a, xa b, xb p p σab d, xd D hadrons

  5. JET RECONSTRUCTION • Jet = collimated spray of high energy hadrons • Interplay between theory and experiment: • THEORY: “calculate” the real jet • EXPERIMENT: measure the jet • Why reconstruct jets? • Full knowledge of jet properties: jet shape, fragmentation functions, energy, … • IDEA: going from tracks and EMC towers to jets •  Jet Finding • Theoretical and experimental issues in Jet Finding • Jet Finding Algorithms • Cone algorithms • KT algorithms Elena Bruna, Yale University

  6. THEORETICAL ISSUES • Required THEORETICAL features in a jet finding algorithm: • Collinear safety: the algorithm should be insensitive to any collinear radiation. • Example A: if the energy is split among soft particles, and each tower is under a threshold, the jet is lost • Infrared safety: the algorithm should not be sensitive to soft radiation BAD: 2 jets are merged in one OK Elena Bruna, Yale University A • Example B: if the energy of a parton is split in two towers, and the algorithm starts with the particles with highest E, a different jet may be found B

  7. EXPERIMENTAL ISSUES • Required EXPERIMENTAL features in a jet finding algorithm: • Detector independence: the performance of the jet algorithm should not be dependent on detector segmentation, energy resolution, … • Stability with luminosity: jet finding should not be strongly affected by multiple hard scatterings at high beam luminosities. • Fast • Efficient: the jet algorithm should find as many physically interesting jets as possible Elena Bruna, Yale University

  8. CONE ALGORITHM seed tracks or towers • A ‘seed’ defines the approximate jet direction • seed = track with E>Ethreshold • Tracks which are within a radius of R<Rcone are taken (R=√(ΔΦ2+Δη2)) • The centroid of the cone is given by summing the momenta of the particles inside the cone • The centroid becomes the new seed : procedure iterated until the seed position is stable Rcone Elena Bruna, Yale University centroid = new seed Rcone

  9. MIDPOINT CONE ALGORITHM midpoint • PART I: searching midpoint • Search for missing jets using the midpoint of all the pairs of found jets as seed • PART II: splitting/merging • This stage starts once stable cones have been found (see previous slide) • IDEA: disentangle jets which share common towers in the calorimeter Elena Bruna, Yale University JET #2 JET #1 pTjet1>pTjet2 Take the lower-pT jet (#2) f=Eshared/Ejet#2 if f>50% then MERGE jet#1 and jet#2 elseSPLIT the jets

  10. KT JET ALGORITHM • Start with a list of preclusters, i.e. 4-vectors of tracks, and calorimeter towers. Each precluster is defined by: E, p, y. • Calculate: • For each precluster i: • For each pair (i,j) of preclusters: (D is a parameter of the jet algorithm) • Find the minimum of all the di and dij and label it dmin • If dmin is a dij, remove preclusters i and j from the list and replace them with a new merged precluster • If dmin is a di, the precluster i is not “mergeable” and it can be added to the list of jets. • Repeat the procedure until the list of preclusters is empty, i.e. all the jets have been found Elena Bruna, Yale University

  11. RECENT RESULTS AND PERSPECTIVES • Inclusive differential cross section for p+pjet + X measured by STAR with polarized proton beams. • Increased L in 2006: • High-pT jets • PID of jet fragments • GOALS for STAR: 2003-2004 data Elena Bruna, Yale University • Study of the fragmentation functions for particles inside jets in p+p for different jet energies and opening angles • Measure jets in Au+Au • Study the hadrochemical modifications of jets in the nuclear medium

  12. MIDPOINT CONE JET FINDING IN p+p IN STAR • Performance study • DATA: p+p PYTHIA events (2006) • Jet Finder applied to: • PYTHIA particles  PYTHIA Jets (no detector effects) • Reconstructed tracks and calorimeter towers  RECO Jets (detector effects) • SETUP for the Jet Finder: • R=0.7 (ϑc~0.49 rad), |ηjet|<0.3 • R=0.5 (ϑc~0.35 rad), |ηjet|<0.5 • R=0.4 (ϑc~0.28 rad), |ηjet|<0.6 • seed: ET>0.5 GeV • PYTHIA Jets vs RECO Jets • Only the leading RECO Jets are considered Elena Bruna, Yale University Experimental acceptance hjet=0.3 JET R=0.7 η=+1 η=-1 z

  13. ENERGY RESOLUTION (1 of 2) 10<E(PYTHIA)<10.3 GeV 20<E(PYTHIA)<20.5 GeV BLACK = RECO jet RED = PYTHIA jet BLACK = RECO jet RED = PYTHIA jet 30<E(PYTHIA)<30.5 GeV Elena Bruna, Yale University BLACK = RECO jet RED = PYTHIA jet R=0.7

  14. ENERGY RESOLUTION (2 of 2) R=0.7 Elena Bruna, Yale University

  15. MULTIPLICITY OF JET FRAGMENTS (1 of 2) 10<E(PYTHIA)<10.3 GeV 20<E(PYTHIA)<20.5 GeV 30<E(PYTHIA)<30.5 GeV Elena Bruna, Yale University BLACK = RECO jet RED = PYTHIA jet R=0.7

  16. MULTIPLICITY OF JET FRAGMENTS (2 of 2) 10<E(PYTHIA)<10.3 GeV all particles charged particles Elena Bruna, Yale University BLACK = RECO jet RED = PYTHIA jet neutral particles R=0.7

  17. JETS IN VACUUM • MLLA (modified leading logarithmic approximation) formalism provides a good description of fragmentation functions in e+e- and ppbar collisions. e+e-√s=29 GeV H. Aihara et al. (TPC/2g coll.), PRL 52, 577 (1984) • STAR p+p 2006 data: • Measure fragmentation functions in p+p at 200 GeV as baseline for Au+Au • test pQCD models (MLLA, …)

  18. JET QUENCHING IN HOT NUCLEAR MATTER • Signatures: • Modification of jet energy distributions • Modification of jet fragmentation functions • Modification of the hadrochemical composition of the jet fragments [Sapeta, Wiedemann arXiv:0707.3494] • Medium-modified MLLA (includes hadrochemistry predictions): • IDEA: in-medium gluon radiation implies an enhancement of the parton splitting • MODEL: the parton splitting functions are enhanced by a common factor [Sapeta, Wiedemann arXiv:0707.3494] Elena Bruna, Yale University

  19. MODEL PREDICTIONS [Sapeta, Wiedemann arXiv:0707.3494] Elena Bruna, Yale University Full jet reconstruction and PID inside jets in both p+p and A-A is required

  20. JETS ON REAL DATA: p+p (2006) • p+p 2006 data set: • Luminosity ~8.7 pb-1 • 8.3 M Jet Patch events • STAR Triggers: • MinBias: • Beam-Beam-Counter (BBC) • High Tower: • BBC + 1 tower (0.05h x 0.05 f) with ET>5.4 GeV • Jet Patch: • BBC+ 20x20 towers (patch, 1h x 1 f) withET>8 GeV Elena Bruna, Yale University

  21. ξDISTRIBUTIONS FOR CHARGED HADRONS (1 of 2) • 2 jet energies: • 30<Ejet<40 GeV • 40<Ejet<50 GeV • x distributions compared with PYTHIA simulations Elena Bruna, Yale University Very good agreement between data and PYTHIA

  22. ξ FOR CHARGED HADRONS (1 of 2) Elena Bruna, Yale University

  23. SUMMARY AND OUTLOOK • Full jet reconstruction in p+p at RHIC is needed as a baseline to study hadrochemical modifications of jets in Au+Au collisions • The standard jet finding algorithm (midpoint cone) has been tested on PYTHIA events with different settings of the parameters (seed, Radius) • Test other algorithms: KT, … • Analysis on p+p (run 2006): in progress • Fragmentation functions: charged particles, p, K, π, e, Λ, … Elena Bruna, Yale University

  24. EXTRA SLIDES Elena Bruna, Yale University

  25. TRIGGER BIAS: JET PATCH VS HIGH TOWER Elena Bruna, Yale University

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