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semileptonic ttbar + jet events

Kenneth Wraight. semileptonic ttbar + jet events. pt spectra of extra jets. Motivations. Study top quark properties: charge, spin, etc.

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semileptonic ttbar + jet events

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  1. Kenneth Wraight semileptonic ttbar + jet events pt spectra of extra jets

  2. Motivations Study top quark properties: charge, spin, etc. Background to associated Higgs production and multi-jet SUSY decays (10s of ttbar events per second for σ=833pb at L=1034cm-2s-1): must be controlled. Ultimate test of generation and simulation software: multiscale QCD calculations

  3. 4/9 fully hadronic, 4/9 semileptonic, 1/9 fully leptonic Golden channel: semileptonic ttbar decays Looking for 4 jets from the hard process: 2 b quarks and 2 hadronic W daughters (↑Tevatron’s favourite (~85%)) LHC: ~90% gluon-gluon production. expect over 1million events per year @ 1034cm-2s-1

  4. standard commissioning • trigger: • isolated electron Ptel>25GeV or isolated muon Ptmu>20GeV • analysis cuts: • exactly one good* lepton <-remove dilepton events • Etmiss>20GeV <- neutrino • >3 good** jets with Ptjet>20GeV • >2 good** jets with Ptjet>40GeV • (n b-tagged jets (n=1 or 2)) * electron or muon with Ptlep>20GeV within |η|<2.5 fulfilling isolation requirement Et,ΔR=0.2<6GeV (not yet implemented). Also ignore electrons in crack region 1.37<|η|<1.52. ** jets with Ptlep>20GeV within |η|<2.5. Also remove jets with good electron nearby i.e. ΔRj-el<0.2.

  5. Systematics • luminosity • no. of events per bunch crossing • reconstruction related: calibrating calorimetry, jet energy and energy flow • beam tests, minimum bias, single particles; Z(γ)+jet events; associated tracker, calorimetry & muon studies • btagging efficiency & fake rates • calibrate using ttbar events • lepton identification & and energy scale • use Z and meson decays, less crucial than W mass measurement • Theory systematics as important as instrumental.

  6. Defining and testing reconstruction efficiency • (easy part) Test: split the sample (5212) in two • first set of events is used to study efficiency of triggers, simulation reconstruction and analysis cuts using generator level information to match jets to partons. • second set is analysed independently of generator information and scaled using the efficiencies obtained from first sample • (controversial part) Define ‘matching’: two ways • Nikhef method: Compare reconstructed top vector to generated top vector. If ΔR<0.4, then declare top well-matched • ‘Glasgow method’: Match generated top daughter partons to shower jets (ΔR<0.4, dE<100GeV) then on to simulated jets (ΔR<0.4, dE<100GeV). If top is reconstructed from jets matched to generator level parton, then declare top well-matched What’s ‘The best’ method of matching?

  7. efficiencies • ξsel = no. passing trigger & selection cuts / number of sample events • depends on triggering and selection criteria • ξmatch = no. of events matched / no. passing trigger & selection cuts • matching procedure • ξgood = no. of events ‘correctly’ reconstructed / no. of events matched • combinatorics (, some selection) • ξglobal = ξsel * ξmatch * ξgood =no. of events ‘correctly’ reconstructed / number of sample matched

  8. 2798 electronic events selected and 3277 muonic. Com matched events: 5952, alt matched: 1417, overlap:1366 efficiencies ctd. • Nikhef: ξglobal = ξsel * 45.42 * 41.23 = ξsel * 18.73 = 3.06% • closest b-jet: ξsel * 18.83 = 3.08% (6066 events) • W mass constraint: ξsel * 25.31 = 2.5% (3666 events) • both: ξsel * 25.78 = 2.55% • Gla1: ξglobal = ξsel * 10.85 * 27.96 = ξsel * 3.03 = 0.50% • closest b-jet: ξsel * 3.03 = 0.50% • W mass constraint: ξsel * 2.84 = 0.46% • both: ξsel * 2.80% = 0.46% • Gla2*: ξglobal = ξsel * 9.11 * 27.67 = ξsel * 2.52% = 0.41% • closest b-jet: ξsel * 2.52 = 0.41% • W mass constraint: ξsel * 3.93 = 0.39% • both: ξsel * 3.82 = 0.38% *additional requirement matched b-jet must have suitable ‘TruthLabel’ ξsel= 16.37, for 37066 event sample (less than a day @ 1034cm-2s-1)

  9. top mass spectra top left: Nikhef matched and recon. above: Glasgow matched and recon. left: all reconstructed tops black: standard, red: W constraint, blue: closest b-tagged jet, pink: both

  10. Generators • MC@NLO: • exact predictions for fixed orders • higher order Matrix Elements with NLO accuracy to describe inclusive rates and LO accuracy for +1 jet • Alpgen & Sherpa: • approximate predictions for all orders (LL accuracy for FS + N jets) • Consistent merging of LO MEs with shower MCs for multiparton FSs MLM for Alpgen and CKKW for Sherpa. MLM avoids double counting by splitting generation sample into exclusive multiplicities for 0->N-1 then recombine sample along with an inclusive N-jet sample. CKKW uses Sudakov form factors to control jet production by reweighting the matrix element. Significant differences?

  11. Jets from “well-reconstructed top” events: b-vetoed and not part of top tri-jet Extra jet Extra jet pt spectra generator comparisons MC@NLO ~25k 5200 sample Fullsim Pjets(red) Fastsim AFjets (blue) Alpgen Fastsim (pink) • Of the original 53,286 event inclusive sample (comparable to ~43,000 ttbar event in first 2 months @ 1031cm-2s-1): • 18603 matched from Gen.Level to AtlFast (~35%) • 13867 were reconstructed (~26%)

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