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Status of the Hadronic Top Search

Status of the Hadronic Top Search. P. Azzi, A. Castro, G. Cortiana,T. Dorigo, A. Gresele, J. Konigsberg, G. Lungu, A. Sukhanov. The all hadronic channel Samples: data and MonteCarlo Kinematical Selection Tag Rate Background and Systematics Btagging Efficiency Cross section

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Status of the Hadronic Top Search

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  1. Status of the Hadronic Top Search P. Azzi, A. Castro, G. Cortiana,T. Dorigo, A. Gresele, J. Konigsberg, G. Lungu, A. Sukhanov • The all hadronic channel • Samples: data and MonteCarlo • Kinematical Selection • Tag Rate • Background and Systematics • Btagging Efficiency • Cross section • Conclusions Ambra Gresele

  2. Dataset and kinematical selection optimization • Dedicated trigger: N(jet)>=4 with Et>=15 and Et>125 GeV • Lint~ 165pb-1 with all relevant subdetectors on and ok • RAW and CORRECTED (L7) JET energy information used • Signal MC: Herwig + Detector Simulation + TrigSim • We apply some pre-requisites for a minimal clean-up of the sample (see CDF Note 6808) • Optimization of S/Bfor jet multiplicity >=6 and for the following quantities (see CDF Note 6808) with these results: • Et  320 GeV • Et/ŝ (centrality)  0.77 • (Aplanarity + 0.0037 x 3Et) 0.85 Ambra Gresele

  3. Summary table of kinematical selection using at least 6 jets Ambra Gresele

  4. Systematics on the kin. selection incl = (6.3  0.04 (stat)  1.9 (syst)) % Ambra Gresele

  5. Secondary Vertices (btags) …. We use SECVTX (Summer 2003) in a method 1 line approach: • define a tag rate and a parametrization which can provide a bgr estimate • compare positive OBServed tags to EXPected tags from the tag rate parametrization • We do so before the application of any kinematical selection and derive a systematic uncertainty on the bgr evaluation • Finally we apply a tight kinematical selection and look for an excess of tags w.r.t. the bgr as expected from top Ambra Gresele

  6. Tag Rate vs Et, Eta, Ntrk and Apla Eta 3 bins Et 8 bins Ntrk 11 bins Apla 8 bins Ambra Gresele

  7. Summary table for the parametrization (81133) Ambra Gresele

  8. (OBS – EXP) / EXP vs Jet Multiplicity • If we plot the ratio: (OBS – EXP) / EXP as a function of the jet multiplicity ( for 4, 5, 6 or  7) • Tags EXP is consistent with OBS Ambra Gresele

  9. Systematics uncertainties on the bgr estimate Different control samples : • pick events with the SMALLEST possible presence of ttbar events • highly populated Three cases: • 2 with reverse cuts • 1 check stability Ambra Gresele

  10. Systematics on Njet If we compare OBS and EXP for Njet  6, (Apla+0.0037x3Et)  0.85, Centrality  0.77 and Et  320 GeV we see that: • Nobs = 3542 • Nexp = 3550  47 • (Nobs – Nexp)/Nexp = (0.2  0.1)%  We consider a systematic uncertainty on Njet = 0.2 % Ambra Gresele

  11. Systematics on Et, … We consider all events with 5 jets and (Apla+0.0037x3Et)  0.85, Centrality  0.77. Systs. on Et 1.4 % 1.6 ALLOW SLOPE 0.6 CONVOLUTION Ambra Gresele

  12. … on (Apla+0.0037x3Et)and onCentr. Syst. (Apla+0.0037x3Et) = 4.7 % Syst. Centr. = 0.3% 1.2 1.4 0.8 0.6 Ambra Gresele

  13. Systematics on Inst.Luminosity, … We consider as a control sample all events with 4 jets. Syst. Inst. Lum. << 1 % Ambra Gresele

  14. … on Run #and onjet- Syst. Run # << 1% Syst. jet- << 1% Ambra Gresele

  15. Total Systematic uncertainty … Ambra Gresele • We now combine all systematics (sum in quadrature): • Njet : 0.2 % • SumEt : 1.4 % • Centrality : 0.3 % • (Apla+0.0037x3Et): 4.7 % • Inst. Luminosity: << 1.0 % • Run #: << 1.0 % • Jet- : << 1.0 % Total systematic uncertainty: ~ 5 %

  16. Background estimate after KIN SEL Ambra Gresele

  17. … some preliminary results For Njet  6 jets (SIGNAL REGION) we see : • Nobs = 326 tags • Nexp(bgr) = 278.0  2.8 (stat)  13.9(syst) tags  Nobs–Nexp = 48.0  14.0 tags Ambra Gresele

  18. Btagging Efficiency We can follow two methods: • factorization method where overall,evbtag =  evtbtag + (1 - evtbtag)  evtmistag with  evtbtag = F2b  btag  SF  (2- btag  SF) + F1b  btag  SF and SF = 0.86  0.07. We have done a cross-check with the single lepton analysis (following CDF Note 6598) • counting method where we degrade the tagged jets with the SF If we compare the two methods they give consistent results. Ambra Gresele

  19. Btagging efficiency per event and per jet Eff. b-evt = (59.3  3.7) % Eff. b-jet = (73.7  6.0) % If NO matching with b-jet: Eff. jet = (83.7  8.2) % Ambra Gresele

  20. Efficiencies plot Ambra Gresele

  21. Cross Section The presence of tt events in the pretag sample leads to an overestimate of the background. We account for it with an iterative procedure and then we rescale the background: • Nexp’ = Nexp ((N – Ntt) / N)pretag = 266.1 and the corrected excess would be: • Nobs- Nexp ‘ = 326 – 266.1 = 60 Ambra Gresele

  22. Final summary table Ambra Gresele

  23. We build the following likelihood function: with the following input values: b’=b*(N-Ntt)/N N=pretag events Ntt=pretag tt events Ambra Gresele

  24. The maximization of the likelihood gives, as central value: • The cross section (iterative) amounts to: Ambra Gresele

  25. Conclusions First full pass with Run I method top cross section. To do next … • brush up the systematic especially jet energy scale and state of the cut PSR, FSR and PDF • Seek preblessing next March … Ambra Gresele

  26. Kinematic cuts optimization: Apla vs 3Et We reject the bottom left corner. By cutting on Aplanarity + K x 3Et. We pick up the best value for k and look for the maximum of S/B mj Aplanarity tt Projection SumEt3 Optimization Ambra Gresele

  27. Kinematic cuts optimization: Centrality After the cut (Aplanarity + 0.0037 x 3Et)  0.85 we findthe best value to cut on the Centrality S/B mj S/B Ambra Gresele

  28. Kinematic cuts optimization: Et After the cut (Aplanarity + 0.0037 x 3Et)  0.85 and Centrality  0.77 we findthe best value to cut on Et S/B tt mj S/B Ambra Gresele

  29. Parametrization using matrix of Jet50 As a cross-check , we apply the matrix of Jet50 on our data sample even if it is not much appropriate because: • it comes from a sample with 2 jets and low SumEt and we use a data with at least 6 jets at higher SumEt • the statistic of the Jet50 sample is very small in our signal region and this is reflected in the bigger Nobs-Nexp/Nexp Ambra Gresele

  30. Negative Tags before and after kin. sel. Ambra Gresele

  31. Cross Check with matrix from Jet50 Ambra Gresele

  32. Systematics on Et, Centr and (Apla+0.0037x3Et) Ambra Gresele • for events passing the kin. sel., we drop the 6th jet and reconstruct new Et, Centr and (Apla+0.0037x3Et) distributions (“6-to-5,, distributions) • in the corresponding control sample we fit the distributions of the Nobs/Nexp ratio with a first degree polynomial • convolute the polynomial function with the corresponding “6-to-5,, normalized distribution • the integral of the convolution gives the total systematic uncertainty

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