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Search for new physics close to the top quark production in the muon+jets channel

Search for new physics close to the top quark production in the muon+jets channel

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Search for new physics close to the top quark production in the muon+jets channel

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  1. Search for new physics close to the top quark production in the muon+jets channel R. Chierici, F. Fassi, S. Perries, S.Tosi CNRS/CC-IN2P3 Lyon, France PCI2009 Workshop, Rabat, Morocco 4-6th October 2010 Farida Fassi

  2. Outline • Motivation • Dedicated MC production • Event selection and efficiency • Event reconstruction • χ² sorting method • Kinematic fit (parameterisation of jets resolution) • Distribution of the Mtt spectrum • Systematic uncertainties • Conclusion Farida Fassi

  3. Motivation • The top quark is “strongly” coupled with the Higgs sector and can provide us with excellent insights on new physics, especially for what concerns the EWSB sector • New physics in the top sector may happen in production, decay, association • Most of the new physics signals in the top sector will distort mtt • Studying the invariant mass of the top pair system is particularly important in many aspects: • As a measure within the SM • top quark pair kinematics • indirect probe of the top quark mass • Undiscovered heavy s-channel resonances can decay to a pair of boosted top quarks: • MSSM Higgs (spin 0) (H/A, if mH,mA>2mt, BR(H/A→tt)≈1 for tanβ≈1) • Technicolor Z’, strong EW SB, Topcolor (spin 1) • KK excitations (spin 2) Farida Fassi

  4. Motivation cont’d • Many models predict resonances in production, for which a spectacular signature would be a peak in mtt • Different spin states and different widths are possible • Analysis strategy • Set-up an analysis as much model independent as possible • Study performance in the important case of a narrow Z’ “Z-like” resonance • Focus on semi-leptonicmuon channel for reasons of simplicity Farida Fassi

  5. Dedicated MC production • Signal: generic Z’ resonances decaying into tt (MadGraph) • couplings to fermions imposed by hand to be the same as those of the Z • different mass points at 7 TeVwith masses ranging from 500 GeV to 2 TeVand for small (Z’=1%mZ’) widths • All final state simulated, but only the semileptonicmuon channel considered in this analysis so far • Backgrounds: • tt+jetsinclusive with ME-PS matching (MadGraph) • also a biased production requiring mtt>1TeV • W/Z+jets(W→ℓ, Z→ℓℓ) (MadGraph) • QCD (PYTHIA) • All background cross sections are rescaled to NLO • predictions, where available. • Other backgrounds, like single top or VV (V=W,Z), • give very small contributions and are neglected Farida Fassi

  6. Event selection • Trigger: • non isolated muon trigger, pT>9GeV @HLT, • gives high efficiency in the whole mass range • Muonselection: • required a good ID muon: • at least 11 hits in the tracker, 2norm<10 • IPT<2 mm, no deposits in ECAL/HCAL larger than MIPS around the μ • Only consider ||<2.1, pT>30 GeV candidate muons • We select events with Rmin(,jet)>0.4(jets with ET>30 GeV ) • this cut optimize to eliminate µ from b-, c- decays + /K in flight decays • Veto a second isolated lepton : • no Electron cantidate with Pt >10 GeV and reliso>0.5 (relative isolation) • Jets selection: • at least 4 jets with ET>35GeV • Jets association based on χ² sorting method • Kinematic fit: • Keep the rest of the selection extremely simple (and cut based) before passing the events to a kinematic fit Farida Fassi

  7. Events yield • Expected data reduction in 100/pb • Selection efficiencies ranging from • 15% to 20% • Main background come from W+jets • S/B is about 1.43 Farida Fassi

  8. Event selection efficiency • Evolution of efficiencies as a function of the generated top pair mass • Done at every selection step, including HLT • Trigger gives non negligible effects • Usual degradation when going towards higher masses • Difference between Z’ and SM ttbar, understandable because of different kinematics characteristics and radiation patterns • The kinematic fit degrades even further at high mass (Check this) SM tt events Z’ events Farida Fassi

  9. Event reconstruction Farida Fassi

  10. Jets selection • Need to control combinatory from many jet • Tuned on parton matched events • Jet combinatorial resolved via a 2 method, • minimizing χ2 Total to get the right jet combination • (2 definition on the backup slides) • 2 discriminate variables: • Leptonicχ2: top mass and angle between b-jet and muon • Hadronicχ2: W and top masses and PT ratio defined as log(Pt(t)/Pt(W)) • Global χ2: transverse momentum of the top pair and HTSystem (scalar PT sum of selected jets divided by up to 8 jets) • Total χ2: the sum of the above • MC values are taken from ttbar signal events where exactly 4 jets were matched to the MC partons (2 b-quarks and 2 light quarks from W decay) Farida Fassi

  11. Jet reconstruction performance • Study of the selection efficiency of the perfect four jets in a parton matched ttbar events, compared to the efficiency of missing either 1, 2, 3 or 4 jets • The performance are then determined on parton-matched SM tt events 4 jets top selection probability per χ² minimisation Farida Fassi

  12. Mtt mass resolution • We are determining dependence of the mass resolution vs the • generated mass • Compare simple reconstruction (with analytic determination of the neutrino momentum) without a full kinematic fit • Tails due to badly selected jets… • At low mass one tend to pick up high pT radiation • At high mass jet merging makes the wrong jet to be a lower Pt one • All samples were put together (yellow curve) • Curve red mttgen <= 600 GeV • Curve blue mttgen > 600 GeV • ttbar, ttbar1TeV, Zp • To improve the mtt mass resolution a kinematic Fit was used Farida Fassi

  13. Event reconstruction: Kinematic Fit • The selected 6 fermions (bqqbµν) are input to a kinematic fit, based on a 2 minimization using “MINUIT” • Fitted parametres: • 16 parameter adjusted in the fit: • ( E, η, φ) ×4 jets • Eμ • ( Px, Py, Pz) neutrinos • Error parameterization: • The input parameterization of the resolutions as a function of energy and  • Determined using top jets, separately for light and b-jets • Constraints : • The constraints are both W mass and top mass • The neutrino is reconstructed via the MET and the W mass constraint, keeping the solution giving the best top mass • When no solution, the MET is rescaled to have one solution Farida Fassi

  14. Resolution in energy Light jets (Wjets) Resolution in energy b-Jets Resolution in φ b-jets Resolution in φ Light jets (Wjets) Resolution in η b-jets Resolution in η Light jets (Wjets) Farida Fassi

  15. Parameterization of jets resolution Farida Fassi

  16. Resolution and Linearity • The linearity and the resolution of the kinematic fit are studied • SM tt and Z’ samples are put together to increase stat • Resolutions are determined with gaussian fits around the max of the distributions • The use of a kinematic fit slightly improves the linearity and significantly the resolution over the whole mass range of interest • Linearity then used to calibrate the results Farida Fassi

  17. Reconstructed mtt distribution • The ability of reconstructing resonances is different in different regimes of • invariant mass • Progressive loss of ability in reconstructing a peak due to jet merging at high top boosts • Normalization: • Z’→tt→bqqbℓ cross section normalised to SM tt →bqqbℓ • Qcd is muon enriched(15GeV) • mtt is corrected using the linearity curve Farida Fassi

  18. Systematic studies Jet Energy Scale (JES): – All the jets are shifted coherently assuming a bias of ±10% – For each tested bias : all the analysis is redone Jet Energy Resolution: – Each jet is randomly shifted – add 5% in the jet resolution in a correlated way, using a smearing assuming a Gaussian degradation – For each point : all the analysis is redone Luminosity: all yields changes by ±10% in a correlated way Z’ 1TeV TTbar Farida Fassi

  19. Systematic studies • Variation in the acceptance dominated by JES uncertainties Farida Fassi

  20. Conclusion • An analysis to measure mtt, focusing on the spectrum region not so far from top pair threshold production was presented • We favored the improvement of the mass resolution via a full event reconstruction using a kinematic fit helps in this respect • We studied the applicability of this method in the search for new physics in top-pair production • The relevant case of a narrow width Z-like Z’ was analyzed in detail • Commissioning of method has already started with first data Farida Fassi

  21. Backup Farida Fassi

  22. Trigger 2mt<mtt<500 GeV 0.5 TeV<mtt<1.5 TeV mtt>1.5 TeV The kinematics of tt events drastically change as a function of mtt. • Leptons progressively lose t heir isolation • Individual jet reconstruction becomes an issue because of jet merging Need of a robust triggering scheme: • non isolated muon trigger, pT>9GeV @HLT, gives high efficiency in the whole mass range • possibility of also adding a jet trigger (not investigated nor exploited) Preferable to have an event selection that maintain good efficiency at high values of mtt Farida Fassi

  23. The 2 sorting method: definitions • The method in a nutshell: 1. Keep up to 8 jets in the event (ET cut at 20 GeV on all the jets) for making a choice. That keeps 92% of good combinations 2. Choose 4 jets out of N, 4N8, with a 2 sorting method that uses MC. The 2 accounts for W and top reconstructed masses, the angle between b and , general quantities like PT(top) and HT. Central values, resolution are taken from MC (not PDG!), and vary with mtt at most by 10%. 3. Give the 4 jets to the kinematic fit Farida Fassi

  24. Neutrino reconstruction • ν is reconstructed using theμ, MET and the b-jet • Using: • Pt(ν)=MET Pz(ν) is computed • mW= m(μ+ν) • 2 solutions/event OR no solution • in case of 2 solutions: we choose the Pz that optimizes mtop=m (μ+ ν+b-jet) • in case of no solution: we rescale the MET until we have a solution Farida Fassi