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xAOD

  • check containers and key names in xAOD
checkxAOD.py xAOD.pool.root
  • check variables inside the containers
root xAOD.root
root [1] CollectionTree->Print("CotainerName*")

Analysis

For HQTTtResonanceTools

Large Jet Tagging

  • We can select NLARGEJETTTRESSTTAU32_N or NLARGEJETTTRESSTMASS_N
  • In default setting smooth cut (WP 80%) is applied.
  • In this selection Lepton is required (Get the highest pT narrow jet "near" the lepton = selJet)
  • "good" large jet have "pT > 300 GeV","|eta| < 2.0","std::fabs(top::deltaPhi(*largeJet, *lep)) > 2.3","top::deltaR(*largeJet, *selJet) > 1.5)"

How to get variables of ungroomed Jet

const ElementLink<xAOD::JetContainer>& linkToUngroomed  =
     jetPtr->getAttribute<ElementLink<xAOD::JetContainer> >("Parent");
const xAOD::Jet* ungroomedJet1 = *linkToUngroomed;
  • In the case "AntiKt10LCTopoJets" is Ungroomed Jet, so you should add "AntiKt10LCTopoJets" container in DxAOD.
  • check # of ungroomed track jet. whether the variable is true or false.(2015.12.08)

optimization of selection

  • There are some kinds of assessing sensitivity. I should choice best equation for each situation.
  • One of sensitivity equation is /sigma = S/(Q/2 + sqrt(B)). (S : Signal yields, B : Background yields, Q : Quality of sensitivity = signal intensity)
    The equation is stable even if B = 0.
  • I should calculate each bins if I want to precisely calculate sensitivity. Then I should check stability of those selection (given by the equation).
  • I should not use selection if the selection is at steep sloop. The errors at steep sloop are very big.

Lepton Veto in EXOT7

  • electron: skimmed pass tight selection && pT>30GeV && |eta|<2.4 && # of electron >= 1
  • muon : skimmed pass DFCommonGoodMuon selection && pT>30GeV && |eta|<2.4 && # of electron >= 1

Event Selections

Preselection

Best Selection(provisional)

  • For 2.5 TeV
    • leading fatjet mass < 210 GeV
    • 2nd leading fatjet mass < 190 GeV
    • leading fat jet track width < 0.7
    • 2nd leading fat jet track width < 0.4
    • # ghost tracks(leading )< 80
    • # ghost tracks(2nd leading )< 110
    • pt_balance < 0.6
    • sensitivity = 0.344802(sig : 11.8689, bg : 1083.88)
  • For 1.5 TeV
    • leading fatjet mass < 210 GeV
    • 2nd leading fatjet mass < 190 GeV
    • leading fat jet track width < 0.6
    • 2nd leading fat jet track width < 0.6
    • # ghost tracks(leading )< 80
    • # ghost tracks(2nd leading )< 110
    • d_eta < 2
    • 2.5 < d_phi
    • pt_balance < 0.5
    • sensitivity = 5.03436(sig : 150.18, bg : 802.466)

Systematics and Statistic uncertainty

  • In QCD_JZ3W events passed event selections are only "8", but # of events should be "O(100)" when events scaled for luminosity(3.34fb-1).
    As the result of this, the sample has large uncertainty.

For Data

  • Trigger menu
  • For Trigger : TRIGDEC HLT_ht850_L1J100 (TRIGDEC = trigger passed, TRIGMATCH =?)

Note

  • sensitivity used # of ghost tracks is better than not used.
  • # of tracks in ungroomed jet -> bat
  • pbook command : retry(RUN#,retryBuild=True,newSite=True)

To do List

  • Check the method of tau32 selection whether I should use pT dependency cuts or not.
  • Check top::deltaPhi(*largeJet, *lep) and top::deltaR(*largeJet, *selJet)
  • Check the procedure of HepTopTagger.

-- ShotaSuzuki - 2015-11-18

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Topic revision: r21 - 2015-12-14 - ShotaSuzuki
 
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