Semileptonic ttbar jet events
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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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Semileptonic ttbar jet events

Kenneth Wraight

semileptonic ttbar + jet events

pt spectra of extra jets


Motivations

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


Golden channel semileptonic ttbar decays

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


Standard commissioning

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.


Systematics

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.


Defining and testing reconstruction efficiency

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?


Efficiencies

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


Efficiencies ctd

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)


Top mass spectra

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


Generators

Generators

  • [email protected]:

    • 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?


Extra jet

Jets from “well-reconstructed top” events: b-vetoed and not part of top tri-jet

Extra jet

Extra jet pt spectra generator comparisons

[email protected] ~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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