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Tiles Method with new variables. Search for new discrimination variables. Tiles Method assumptions:. SM background fractions predicted by MC simulation (2D PDF is known) Uncorrelated discriminating variables for signal events Different distributions of events for signal and background.
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Search for new discrimination variables Tiles Method assumptions: • SM background fractions predicted by MC simulation (2D PDF is known) • Uncorrelated discriminating variables for signal events • Different distributions of events for signal and background Variables need low correlation coefficient ρ and high separation power <S2>
Search for new discrimination variables: Old Variables • Main background for one-lepton search channel: Tops (semi- & full-leptonic) • Good variable for semi-leptonic Top-Bkg: • transverse mass • MT > 110 GeV – Cut • Good reduction of semi-leptonic • Top Events • we use MT=110 GeV to separate control • and signal region • Good variable for SUSY with missing energy: effective mass • signifies “activity” of event However: MT and Meff are correlated!
New discrimination variables: Definitions • transverse mass: MT • effective mass: Meff • effective mass w/o lepton pT : Meff’ • effective mass w/o hardest jet pT: Meff2 • effective mass w/o lepton pTand hardest jet pT: Meff2’ • sum of jet pT and lepton pT w/o hardest jet pT: HT2 • sum of jet pT w/o hardest jet pT: HT2’ • hadronic top mass (inv. mass of 3 jets with largest vector-summed pT): HTM • leptonictop mass (leptonic W + jet, s.t. inv. mass isclosest to mtop):MTop • missing transverse energy: MET • angle between 1st and 2nd hardest jet: PhiJ12 • lepton transverse momentum : pTlep
New discrimination variables: Correlation and Separation SU3: All SUSY benchmark points:
Effective mass distributions Applied on data after event selection but w/o the cut MT > 110 GeV Effective Mass (with lepton pT): Meff Effective Mass w/o lepton pT: Meff’
Invariant top mass distributions Applied on data after event selection but w/o the cut MT > 110 GeV Leptonic Top Mass: MTop Hadronic Top Mass: HTM
Procedure: • Fit SM and SIGNAL Samples with 2D unbinned kernel estimation method (RooFit) for the new variables: mT ,Meff’ • 2. Toy Event Generation: • 10 000 000 SM Events • 5 000 000 SIGNAL Events • 3. Apply Tiles Method on 5000 toy experiments, each representing 1 fb-1 of SM background and SUSY signal PDF’s for SM and SUSY
5000 Toy Experiments, each 1 fb-1 SM + SU3: Est. Signal Events MT & Meff MT & Meff’ 2x2 pull
5000 Toy Experiments, each 1 fb-1 SM + SU3: Est. Signal Events MT & Meff MT & Meff’ 8x8 12x12
5000 Toy Experiments, each 1 fb-1 SM +SU3: Tiles Configurations MT & Meff MT & Meff’ Bias σstat
Why doesn’t the stat. error increase for higher Tiles Configurations? • Checked the updated Tiles Method on the old toys with MT and Meff • σstat does not increase for higher n • not an effect of new variables • not an effect of toy generation What has been changed in the Tiles Method? • corrected log-likelihood estimator: • parameters of Fitting Method • (start values, step size) • generalization of Fitting Method to 3D • (cross-check seemed to be okay) … in progress!