Bayesian Learning of Neural Networks for Signal/Background Discrimination in Particle Physics

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dc.creatorPogwizd, Michael
dc.creatorElgass, Laura Jane
dc.creatorBhat, Pushpalatha C.
dc.date2007-07-06
dc.date.accessioned2026-07-07T08:14:18Z
dc.date.available2026-07-07T08:14:18Z
dc.descriptionNeural networks are used extensively in classification problems in particle physics research. Since the training of neural networks can be viewed as a problem of inference, Bayesian learning of neural networks can provide more optimal and robust results than conventional learning methods. We have investigated the use of Bayesian neural networks for signal/background discrimination in the search for second generation leptoquarks at the Tevatron, as an example. We present a comparison of the results obtained from the conventional training of feedforward neural networks and networks trained with Bayesian methods.
dc.description3 pages, 4 figures, conference proceedings
dc.identifierhttps://arxiv.org/abs/0707.0930
dc.identifierhttp://arxiv.org/abs/0707.0930
dc.identifier.urihttp://salesiana.dossiersoluciones.com/handle/123456789/133073
dc.subjectData Analysis, Statistics and Probability
dc.titleBayesian Learning of Neural Networks for Signal/Background Discrimination in Particle Physics
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