EPJ Web Conf.
Volume 158, 2017The XXIII International Workshop “High Energy Physics and Quantum Field Theory” (QFTHEP 2017)
|Number of page(s)||9|
|Section||Detectors and Data Processing for Future Experiments in High Energy Physics|
|Published online||24 October 2017|
Deep Learning Neural Networks and Bayesian Neural Networks in Data Analysis
Skobeltsyn Institute of Nuclear Physics M.V. Lomonosov Moscow State University, Moscow 119991, Russian Federation
Published online: 24 October 2017
Most of the modern analyses in high energy physics use signal-versus-background classification techniques of machine learning methods and neural networks in particular. Deep learning neural network is the most promising modern technique to separate signal and background and now days can be widely and successfully implemented as a part of physical analysis. In this article we compare Deep learning and Bayesian neural networks application as a classifiers in an instance of top quark analysis.
© The authors, published by EDP Sciences, 2017
This is an Open Access article distributed under the terms of the Creative Commons Attribution License 4.0, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Initial download of the metrics may take a while.