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H  WW  2m2n

H  WW  2m2n. We’re using a combination of 3 BDTs As the variables used for training have different shapes for each background , we tried to extract the maximum information performing 3 independent trainings against the main backgrounds : WW, ttbar and Zmumu. BDT anti ttbar.

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H  WW  2m2n

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  1. HWW2m2n • We’reusing a combination of 3 BDTs • As the variables usedfor training havedifferentshapesforeachbackground, wetriedtoextractthemaximuminformationperforming 3 independent trainings againstthemainbackgrounds: WW, ttbar and Zmumu. BDT anti ttbar BDT anti WW BDT anti Z

  2. Signal and backgrounds are evaluatedbythe 3 BDTs signaltendstopeaktowards (1,1,1) and backgroundstowards (-1,-1,-1) As a firstapproachweapply a sphericalcutaroundtheregionwithhighestsignalcontentoverbackground. Theradius of thisspherewillbethe final discriminant variable. HWW130 WW ttbar Zmumu Examplefor mH=130 GeV 200 pb-1@ 10 TeV X axis : BDT1 response Y axis: BDT2 response Z axis: BDT3 response

  3. Data vs MC comparisonwith 36 pb-1 Examplefor HWW160 Nextstepwillbeto re-traintheBDTsusing the new Fall10 MC samples

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