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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 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
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
Summary table of kinematical selection using at least 6 jets Ambra Gresele
Systematics on the kin. selection incl = (6.3 0.04 (stat) 1.9 (syst)) % Ambra Gresele
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
Tag Rate vs Et, Eta, Ntrk and Apla Eta 3 bins Et 8 bins Ntrk 11 bins Apla 8 bins Ambra Gresele
Summary table for the parametrization (81133) Ambra Gresele
(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
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
Systematics on Njet If we compare OBS and EXP for Njet 6, (Apla+0.0037x3Et) 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
Systematics on Et, … We consider all events with 5 jets and (Apla+0.0037x3Et) 0.85, Centrality 0.77. Systs. on Et 1.4 % 1.6 ALLOW SLOPE 0.6 CONVOLUTION Ambra Gresele
… on (Apla+0.0037x3Et)and onCentr. Syst. (Apla+0.0037x3Et) = 4.7 % Syst. Centr. = 0.3% 1.2 1.4 0.8 0.6 Ambra Gresele
Systematics on Inst.Luminosity, … We consider as a control sample all events with 4 jets. Syst. Inst. Lum. << 1 % Ambra Gresele
… on Run #and onjet- Syst. Run # << 1% Syst. jet- << 1% Ambra Gresele
Total Systematic uncertainty … Ambra Gresele • We now combine all systematics (sum in quadrature): • Njet : 0.2 % • SumEt : 1.4 % • Centrality : 0.3 % • (Apla+0.0037x3Et): 4.7 % • Inst. Luminosity: << 1.0 % • Run #: << 1.0 % • Jet- : << 1.0 % Total systematic uncertainty: ~ 5 %
Background estimate after KIN SEL Ambra Gresele
… 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
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
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
Efficiencies plot Ambra Gresele
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
Final summary table Ambra Gresele
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
The maximization of the likelihood gives, as central value: • The cross section (iterative) amounts to: Ambra Gresele
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
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
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
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
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
Negative Tags before and after kin. sel. Ambra Gresele
Cross Check with matrix from Jet50 Ambra Gresele
Systematics on Et, Centr and (Apla+0.0037x3Et) Ambra Gresele • for events passing the kin. sel., we drop the 6th jet and reconstruct new Et, Centr and (Apla+0.0037x3Et) 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