Type of Document	Dissertation
Author	Kau, Daekwang
Author's Email Address	dkau@fnal.gov
URN	etd-11132007-102828
Title	Evidence for Single Top Quark Production Using Bayesian Neural Networks
Degree	Doctor of Philosophy
Department	Physics, Department of
Advisory Committee	Advisor Name Title Harrison B. Prosper Committee Chair Ettore Aldrovandi Committee Member Jorge Piekarewicz Committee Member Laura Reina Committee Member Todd Adams Committee Member
Keywords	Neural Networks Top Quark Electroweak Bayesian
Date of Defense	2007-08-20
Availability	unrestricted
Abstract We present results of a search for single top quark production in pp collisions using a dataset of approximately 1 fb−1 collected with the DØ detector. This analysis considers the muon+jets and electron+jets final states and makes use of Bayesian neural networks to separate the expected signals from backgrounds. The observed excess is associated with a p-value of 0.081%, assuming the background-only hypothesis, which corresponds to an excess over background of 3.2 standard deviations for a Gaussian density. The p-value computed using the SM signal cross section of 2.9 pb is 1.6%, corresponding to an expected significance of 2.2 standard deviations. Assuming the observed excess is due to single top production, we measure a single top quark production cross section of _(p¯p ! tb+X, tqb+X) = 4.4±1.5 pb.
Files	Filename       Size       Approximate Download Time (Hours:Minutes:Seconds)     28.8 Modem   56K Modem   ISDN (64 Kb)   ISDN (128 Kb)   Higher-speed Access    thesis.pdf 8.07 Mb 00:37:22 00:19:13 00:16:49 00:08:24 00:00:43