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Analysis of non-photonic electrons from Cu+Cu collisions at √ s NN = 200 GeV at STAR.

Analysis of non-photonic electrons from Cu+Cu collisions at √ s NN = 200 GeV at STAR. A. G. Knospe Yale University STAR Collaboration 31 January 2007. slide 1. light. Motivation. M. Djordjevic, arXiv:nucl-th/0310076 (2003). Non-photonic e ± allow the study of heavy flavor

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Analysis of non-photonic electrons from Cu+Cu collisions at √ s NN = 200 GeV at STAR.

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  1. Analysis of non-photonic electrons from Cu+Cu collisions at √sNN = 200 GeV at STAR. A. G. Knospe Yale University STAR Collaboration 31 January 2007

  2. slide 1 light Motivation M. Djordjevic, arXiv:nucl-th/0310076 (2003) • Non-photonic e± allow the study of heavy flavor • Why Study Heavy Flavor? • Heavy quarks produced in initial hard scattering of partons (dominant: ggQQ) • Lower energy loss than light quarks (dead cone effect) • Energy loss in medium depends on: • Quark mass • Gluon densities • Thickness of medium traversed M. Djordjevic, arXiv:nucl-th/0603066 (2006)

  3. slide 2 Heavy Flavor Decays • Must study heavy-flavor decay products • Some studies reconstruct hadronic decays: i.e. D0Kp, D*D0p, D±Kpp, Ds±pf (S. Baumgart) non-photonic e± • We look at semileptonic decay modes: • c e+ + anything (B.R.: 9.6%) • D0  e+ + anything(B.R.: 6.87%) • D e + anything(B.R.: 17.2%) • b e+ + anything (B.R.: 10.9%) • B e + anything(B.R.: 10.2%) • Heavy flavor decays expected to dominate non-photonic (single) e± spectrum; b decays should dominate at high pT • Photonic e± background: • g conversions (pgg, ge+e-) • Dalitz decays of p0, h, h’ • r, f, Ke3 decays (small contributions)

  4. slide 3 Results from Au+Au 200 GeV • Energy loss in medium predicted to reduce non-photonic e± yields • Models (BDMPS, DGLV) predict less suppression than observed • Predicted suppression: factor of ~2 to 2.5 • Best agreement with data (suppression by factor ~5) for ce- only • Collisional energy loss is important arXiv:nucl-ex/0607012 (2006)

  5. slide 4 Cu+Cu: Event Selection • Analyze STAR data from 2005 Cu+Cu 200 GeV run • EMC High Tower Trigger: • At least one tower with E > 3.75 GeV • Enhances yields at high pT Million Events 0 5 10 15 20 25 30 35 • Start with: 30M minimum bias events and 4M high tower events • Event Selection Cuts: • centrality 0-60% • EMC quality check • primary vertex |z| < 20 cm • Analyzed 9M min. bias events and 2M high tower events minbias high tower preliminary

  6. EMC: EMC = Towers + Shower Maximum Detector (SMD) SMD used to identify e±: showers better developed than h± Require hits (> 2 strips) in both the h and f planes of SMD e± in STAR EMC: p/E≈ 1 Use a loose cut: 0 < p/E < 2 TPC: 3.5 < dE/dx < 5 keV/cm Good dE/dx separation between e± and p ± for p > 1.5 GeV/c distance to primary vertex < 1.5 cm 0 < h < 0.7 quality cuts slide 5 electrons hadrons e± Identification EMC SMD Clusters:

  7. EMC: EMC = Towers + Shower Maximum Detector (SMD) SMD used to identify e±: showers better developed than h± Require hits (> 2 strips) in both the h and f planes of SMD e± in STAR EMC: p/E≈ 1 Use a loose cut: 0 < p/E < 2 TPC: 3.5 < dE/dx < 5 keV/cm Good dE/dx separation between e± and p ± for p > 1.5 GeV/c distance to primary vertex < 1.5 cm 0 < h < 0.7 quality cuts slide 5 e± Identification EMC all other cuts passed preliminary

  8. EMC: EMC = Towers + Shower Maximum Detector (SMD) SMD used to identify e±: showers better developed than h± Require hits (> 2 strips) in both the h and f planes of SMD e± in STAR EMC: p/E≈ 1 Use a loose cut: 0 < p/E < 2 TPC: 3.5 < dE/dx < 5 keV/cm Good dE/dx separation between e± and p ± for p > 1.5 GeV/c distance to primary vertex < 1.5 cm 0 < h < 0.7 quality cuts slide 5 e± Identification EMC preliminary e±

  9. slide 6 Corrections from real data • A=Acceptance Correction:Run-by-run correction for EMC acceptance losses • e± inefficiencies: • not reconstructed in TPC • track quality • out of acceptance • fails PID cuts • Embed simulated e± tracks into real Cu+Cu events • r = Reconstruction Eff. dy:fraction of simulated e± reconstructed and identified by cuts • dy = 0.7 because 0 < h < 0.7 preliminary from embedding preliminary

  10. slide 7 Corrections p± from real data • Correct for TPC energy loss separately • Fit ln(dE/dx) projections • purity (a1):fraction of particles within dE/dx cut that are e± • efficiency (a2):fraction of e± that fall within dE/dx cut e± h± preliminary preliminary

  11. slide 8 Photonic e± Background black: e+e- pairs blue: combinatorial background red=photonic e±=black – blue • Dominant sources of photonic e±: • Conversion (g e+e-) • Dalitz decays (p0,h  e+e-) • Photonic e+e- pairs have low invariant mass • Photonic e± identified through invariant mass cut: • Each e± is paired with all oppositely charged tracks in the same event that meet these criteria: pass the track selection cuts(but not EMC or dE/dx cuts) pmin > 100 MeV/c distance of closest approach with e± is < 1.5 cm • Calculate the invariant mass of this pair; e± is rejected as photonic (background) if M(e+e-) < 150 MeV/c2 preliminary invariant mass [GeV/c2]

  12. slide 9 Corrections • Some e± rejected by invariant mass cut due to random combinations with other charged tracks • Find the mean number of times e± is rejected by like-charge partner • Random rejection probability (e1): probability for e± to be randomly rejected • Can calculate from real data or e± embedding preliminary e± Background Rejection Efficiency (e2), centrality 0-60% • Some photonic e± not rejected by invariant mass cut • Embed simulated p 0e+e-decays into real Cu+Cu events • Background rejection efficiency (e2): efficiency to find true conversion partner preliminary

  13. slide 10 Corrected Spectra Corrected spectra: Cu+Cu 200 GeV minimum bias • Apply corrections: preliminary high tower preliminary

  14. slide 10 Corrected Spectra minimum bias • Apply corrections: • inclusive/background: preliminary Au+Au, p+p Cu+Cu high tower preliminary

  15. slide 11 Merging Data Sets • Find the ratio of high tower to minimum bias inclusive e± yields • Fit with S-function • Saturates to Average Prescale • Divide high tower spectra by S-function and average with minimum bias • High tower data only for pT > 4.8 GeV/c • Merged e± spectra preliminary preliminary

  16. slide 12 Nuclear Modification Factor • Calculate non-photonic e±RAA: • Nbinary = 82.2 for centrality 0-60% preliminary preliminary preliminary Cu+Cu spectrum scaled by Nbinary = 82.2

  17. slide 13 Summary • Already done: • Applied Au+Au non-photonic e± analysis techniques to Cu+Cu data for centrality 0-60% • Extracted purity and dE/dx cut efficiency from energy-loss distributions • Extracted efficiencies from small embedding data set • Merged minimum bias and high tower data sets • Computed RAA • To do: • Extract efficiencies from larger embedding data set • Divide into centrality bins preliminary

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