We present a search for standard model Higgs boson production in association with a W boson in proton-antiproton collisions (pp -> W(+/-)H -> l nu bb) at a center of mass energy of 1.96 TeV. The search employs data collected with the CDF II detector that correspond to an integrated luminosity of approximately 1.9 fb(-1). We select events consistent with a signature of a single charged lepton (e(+/-)/mu(+/-)), missing transverse energy, and two jets. Jets corresponding to bottom quarks are identified with a secondary vertex tagging method, a jet probability tagging method, and a neural network filter. We use kinematic information in an artificial neural network to improve discrimination between signal and background compared to previous analyses. The observed number of events and the neural network output distributions are consistent with the standard model background expectations, and we set 95% confidence level upper limits on the production cross section times branching fraction ranging from 1.2 to 1.1 pb or 7.5 to 102 times the standard model expectation for Higgs boson masses from 110 to 150 GeV/c(2), respectively.

Search for standard model Higgs boson production in association with a W boson using a neural network discriminant at CDF RID G-1087-2011 RID E-4473-2011

DELL'ORSO, MAURO;DONATI, SIMONE;PUNZI, GIOVANNI;
2009-01-01

Abstract

We present a search for standard model Higgs boson production in association with a W boson in proton-antiproton collisions (pp -> W(+/-)H -> l nu bb) at a center of mass energy of 1.96 TeV. The search employs data collected with the CDF II detector that correspond to an integrated luminosity of approximately 1.9 fb(-1). We select events consistent with a signature of a single charged lepton (e(+/-)/mu(+/-)), missing transverse energy, and two jets. Jets corresponding to bottom quarks are identified with a secondary vertex tagging method, a jet probability tagging method, and a neural network filter. We use kinematic information in an artificial neural network to improve discrimination between signal and background compared to previous analyses. The observed number of events and the neural network output distributions are consistent with the standard model background expectations, and we set 95% confidence level upper limits on the production cross section times branching fraction ranging from 1.2 to 1.1 pb or 7.5 to 102 times the standard model expectation for Higgs boson masses from 110 to 150 GeV/c(2), respectively.
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