1 / 13

HWW  mnmn

HWW  mnmn. R.González , J.Cuevas,J.Fernández , J.Vizán , L.Lloret , C.Jordá , R.Vilar , J.Duarte ……. Multivariate analysis : BDT. pT of the muons Missing Et Invariant Mass Dphi Transverse mass Dphi ( mu,MET ) Eta of the muons NTracks SumEt. MET. Usual variables.

xyla-thomas
Download Presentation

HWW  mnmn

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. HWWmnmn R.González, J.Cuevas,J.Fernández, J.Vizán, L.Lloret, C.Jordá, R.Vilar, J.Duarte…….

  2. Multivariateanalysis: BDT • pT of themuons • Missing Et • InvariantMass • Dphi • Transversemass • Dphi(mu,MET) • Eta of themuons • NTracks • SumEt MET Usual variables Other variables Mtmax Ptmin Dphi(m,MET) min • Selectionstrategybasedonmultivariateanalysis: BDT • Variables usedfor training the BDT: (after Muon Selection)

  3. -Jet veto notapplied in thisbdtanalysissome variables providing jet informationusedinstead Sum of the energy of all jets in the event with Et>15 GeV, eta<2.4 Number of trackswithsomequalitycuts: -pt>3 GeV -N hits > 5 - |zTrack-zVer|<0.4 cm • Mostimportant variables toreject • ttbarbackground • Variables alreadyincorporatedtothe BDT studyfor CMS Note (seeappendix) • Definition of Ntracks variable willbeupdated in 21X (new qualitycutsfortracksstudied Nhits> 8 , |zTrack-zVer|<0.2 cm). • Study in detail jets withtracks, particleflow, jet Energycorrections… Plansfor 21x

  4. BDT Ranking result (top variable isbestranked) --------------------------------------------------- Rank : Variable : Variable Importance --------------------------------------------------- 1 : Dphill : 2.819e-01 2 : Mtmin : 1.573e-01 3 : etalepmin : 8.728e-02 4 : MET : 7.767e-02 5 : etalepmax : 6.933e-02 6 : InvMass : 6.621e-02 7 : ptlepmin : 4.916e-02 8 : DphiLepMetmin : 4.636e-02 9 : SumEt : 4.154e-02 10 : DphiLepMetmax : 3.520e-02 11 : Mtmax : 3.438e-02 12 : Ntracks : 3.290e-02 13 : ptlepmax : 2.069e-02 TMVA output Training againstttbar Rank : Variable :Variable Importance -- ------------------------------------------------------------- 1 : Ntracks : 2.591e-01 2 : SumEt : 2.091e-01 3 : Mtmin : 8.163e-02 4 : ptlepmin : 8.008e-02 5 : InvMass : 7.526e-02 6 : Dphill : 6.305e-02 7 : etalepmax : 4.709e-02 8: DphiLepMetmin : 3.763e-02 9 : etalepmin : 3.607e-02 10 : Mtmax : 3.565e-02 11 : MET : 3.057e-02 12 : DphiLepMetmax : 2.759e-02 13 : ptlepmax : 1.722e-02 • Ntracks and SumEt • mostuseful variables to • rejecttt • Dphimostuseful variable toreject WW Training against WW Overtraining checked by TMVA Using independent samples Distribution of the variables formainbackgrounds WW and ttbarhavediferentshape 2 independent trainings tried: against WW and ttbar Example: training at mH=160 GeV

  5. Signal Mh=160 WW X axis  output from a BDT trainedagainst WW Y axis  output from a BDT trainedagainsttbar ttbar DY Firstapproach circular cutaround theregionwithhighest s/b • 3 training points : 130, 160, 190 • the resulting functions are combined into a bidimensional one • This method Provides also good rejection against other backgrounds like Z+jets, w+jets… R=sqrt( (x0-bdt1)2 + (y0-bdt2)2)

  6. ExampleformH=160 Distributions of Some variables before and afterapplyingthe BDT cut (maximizingsignificance) InvMass MET InvMass<50 Example of usingthe BDT toestimatecutstobeappliedon a sequentialcutanalysis. Cutvaluesused In thesequential analysis MET>45 (MET>48) dPhi dPhi<1.2(69º) (dPhi<57º) Ptmax Ptmin NTracks Ptmax>30 Ptmax<50 (Ptmax>28) (Ptmax<50) Ptmin>25 (Ptmin>25) Ntracks<10

  7. mH=160 GeV 5s significance around 160 almost achieved with mumu channel ONLY Numbers including fake rate estimation background vs signal events for various cuts on BDT function.

  8. Preliminary results 21x samples (10 TeV) HWW 165: RelVal (CMSSW_2_1_9) TTbar: /TauolaTTbar/Summer08_IDEAL_V9_AODSIM_v1/AODSIM Zmumu: /Zmumu/Summer08_IDEAL_V9_AODSIM_v1/AODSIM low statistics for tt HWW165

  9. HWW 165 HWW 165 TTbar CMSSW_1_6_12 CMSSW_2_1_9 HWW 165 16X: @14 TeV 21X: @10 TeV Zmumu: CSA07 Vs.: HWW 165: RelVal (CMSSW_2_1_9) TTbar: /TauolaTTbar/Summer08_IDEAL_V9_AODSIM_v1/AODSIM Zmumu: /Zmumu/Summer08_IDEAL_V9_AODSIM_v1/AODSIM

  10. Analysis based in sequential cuts for 21x →Started Kinematic variables performingwell Stilltoincludethe pre-selection HWW 165 Relative efficiencies w.r.t. the previous cut at each level

  11. BACKGROUND ESTIMATION WITH DATA Estimation of the number of events from data in the 0 jet bin using soft muons(*), coming from B semileptonic decays in b jets produced in top decays, so basically, estimation of top-antitop fraction, from “well measured” 2 and >=2 jet bins, extrapolate to 0 jet bin. • Simple selection of events: • HWW 2 m selection/isolation • MET>50 GeV • |Mll-MZ|<20 GeV • presence of a soft muon (pT>5 GeV, no isolation required) • With this simple selection, still presence of tW, W+jets, Z+jets and WW in most relevant bins (0-4), at the level of 10-15%. tt dimuons, chowder 1_6 (*) see talk by Dmytro Kovalsky http://indico.cern.ch/getFile.py/access?contribId=2&resId=1&materialId=slides&confId=42770

  12. Next steps for 21x Better estimation of the fake using QCD samples (large statistics) Better estimation of the background contamination with data driven methods Study of muon identification and isolation Improve results in low mass region ….

  13. BACKUP • Trainings y resultados 130,190

More Related