semileptonic ttbar + jet events

pt spectra of extra jets

Kenneth Wraight

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

(↑Tevatron’s favourite (~85%)) LHC: ~90% gluon-gluon production.

Looking for 4 jets from the hard process: 2 b quarks and 2 hadronic W daughters

4/9 fully hadronic, 4/9 semileptonic, 1/9 fully leptonic

expect over 1million events per year @ 1034cm-2s-1

standard commissioning

• trigger:– isolated electron Pt

el>25GeV or isolated muon Ptmu>20GeV

• analysis cuts:– exactly one good* lepton <-remove dilepton events– Et

miss>20GeV <- neutrino– >3 good** jets with Pt

jet>20GeV – >2 good** jets with Pt

jet>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

• 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

• (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

• 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.

• 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)

2798 electronic events selected and 3277 muonic.Com matched events: 5952, alt matched: 1417, overlap:1366

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

• 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?

Extra jetExtra jet pt spectra generator comparisons

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%)

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

MC@NLO ~25k 5200 sampleFullsim Pjets (red) Fastsim AFjets (blue)Alpgen Fastsim (pink)