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Tiles Method with new variables

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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Tiles Method with new variables

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  1. Tiles Method with new variables

  2. 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>

  3. 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!

  4. 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

  5. New discrimination variables: Correlation and Separation SU3: All SUSY benchmark points:

  6. 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’

  7. 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

  8. Toy Experiments

  9. 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

  10. 2D PDF Fit on SM background

  11. 2D PDF Fit on SU3 signal

  12. Agreement of generated Toys with MC Data

  13. 5000 Toy Experiments, each 1 fb-1 SM + SU3: Est. Signal Events MT & Meff MT & Meff’ 2x2 pull

  14. 5000 Toy Experiments, each 1 fb-1 SM + SU3: Est. Signal Events MT & Meff MT & Meff’ 8x8 12x12

  15. 5000 Toy Experiments, each 1 fb-1 SM +SU3: Tiles Configurations MT & Meff MT & Meff’ Bias σstat

  16. 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!

  17. Backup Slides

  18. 18x18 Tiles Configuration: Tiles Content with MC Samples

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