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

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

2007-07-06

arxiv.org/abs/0707.0930v1

Physics

Data Analysis, Statistics and Probability

3 pages, 4 figures, conference proceedings

Scientific paper

Neural 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.

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