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Z' e + e - discovery potential computation

Z' e + e - discovery potential computation. Julien MOREL ATLAS Exotics group IN2P3 – CNRS - LPSC - Grenoble. 18 / 09 / 2007 – CSC dilepton/diphoton. CSC full simulated Z’…. Z’ χ  e + e - a 1 TeV 7250 Z’ with M Z’ = 1 TeV et M ll >500GeV DataSet = 5605. Invariante mass spectrum.

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Z' e + e - discovery potential computation

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  1. Z' e+e- discovery potential computation Julien MOREL ATLAS Exotics group IN2P3 – CNRS - LPSC - Grenoble 18 / 09 / 2007 – CSC dilepton/diphoton

  2. CSC full simulated Z’… • Z’ χ e+e- a 1 TeV • 7250 Z’ with MZ’ = 1 TeV et Mll>500GeV • DataSet = 5605 Invariante mass spectrum Electron energy spectrum Généré Reconstruit Mll (GeV)

  3. di-electron invariant mass spectrum modelisation Background modelisation (DY) Normalisation 1 fb-1 # Event/ 10 GeV • Gpdf depand on the pdf • Fit with a 6 parameters ad-hoc function (Χ2/ndf = 726/557) • Good modelisation between 300 GeV et 6 TeV • DY Pythia • DY (modelisation)

  4. di-electron invariant mass spectrum modelisation Signal modelisation (DY + Z’) Normalisation 1 fb-1 Normalisation 1 fb-1 • DY Pythia • Modélisation DY • Modélisation Z’ (M=3 TeV, Γ=20 GeV) # Evénements / 10 GeV • Only 4 experimental parameters : • The mass • The total decay width • A Z’ amplitude • An interference amplitude • Model independent parameterization # Evénements / 10 GeV Modelisation of a big interference: • Z’χ généré officiel • Modélisation du Z’χ

  5. Z’ performences • Negative tail • ~0.4 % of events • May be a DY systematic (Mgene – Mreco)/ Mgene for 1 TeV Z’ • Gaussian part • ~83 % of events • σ~8.5 GeV • μ~-2.4 GeV • Positive tail • ~ 17 % of events • Include crack events • Non negligible contribution

  6. di-electron invariant mass spectrum modelisation Taking into account the resolution and efficiency Cross check with full simulated 1 TeV Z’ Normalisation 1 fb-1 # Evénements / 1 GeV • Z’ χ reco (CSC simulation) • DY reco (modelisation) • Z’ χ reco (modelisation)

  7. di-electron invariant mass spectrum modelisation Cross check with full simulated 7 TeV Z’ (private production) Fit of generation Reco modelisation from gene fit # Event/ 10 GeV # Event/ 10 GeV • Z’ χ Gene (CSC simulation) • Z’ χ Gene (modelisation) • Z’ χ Reco (full simulation) • Z’ χ Reco (modelisation)

  8. di-electron invariant mass spectrum modelisation We can modelise many Z’ at different mass • Generation with pythia • Fit the generated shape with our parameterization • Include the resolution and efficiency • Z’ χ Gene  Efficiency • Z’ χ Reco (modelisation)

  9. Systematic incertainties • NLO calculation • Effect : Cross section + 20 to 34 % • Mclimit implementation  -0% , +20% • PDF incertainties •  4 à 8 % • Mclimit implementation  5% LHAPDF/CTEQ error sets F.Heinemann ZEUS NLO + NLO MRST NLO + NLO CTEQ NLO + NLO CTEQ LO + LO F.Heinemann

  10. Using MCLimit for 2 TeV Z’ example Normalisation 1 fb-1 -Z’ reco (modelisation) -DY reco (modelisation) -Pseudo data H0 = DY , H1 = 2 TeV Z’ Mass = 2 TeV Width = 24.3 GeV Ampl. Z’ = 499 Ampl. Interference = -793 Few events # Event/ 10 GeV fit from generation Les résultats de MCLimit 5000 pseudo exp. Lumi = 1 fb-1 Using Lumi5s() :We need 0.37 fb-1 are enought to discover this Z’. With 5000 pseudo exp. results may fluctuate a bit. Need more calculation Hypo Z’ Hypo DY -2lnQ

  11. Discovery potentiel • CMS Result • Etude de la découverte du Z’ via : • 6 models: SSM, LRM, ALRM, χ, ψ, η • 3 differentes masses : 1, 3, 5 TeV ATLAS preliminary Preliminary discovery limit For Z’ Chi Int lumi (fb-1) ~5 fb-1 for 3 TeV ~5 fb-1 for 3 TeV Z’ mass (TeV)

  12. Luis result on 4 decembre 2007 fit based approche

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