Evidence for Single Top Quark Production Using Bayesian Neural Networks
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...
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| Format: | Others |
| Language: | English English |
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Florida State University
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| Online Access: | http://purl.flvc.org/fsu/fd/FSU_migr_etd-3296 |
| Summary: | 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. === A Dissertation submitted to the Department of Physics in partial fulfillment of the requirements for the degree of Doctor of Philosophy. === Fall Semester, 2007. === August 20, 2007. === Includes bibliographical references. === Harrison B. Prosper, Professor Directing Dissertation; Ettore Aldrovandi, Outside Committee Member; Todd Adams, Committee Member; Laura Reina, Committee Member; Jorge Piekarewicz, Committee Member. |
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