Revealing Fundamental Physics from the Daya Bay Neutrino Experiment Using Deep Neural Networks [electronic resource].
- Washington, D.C. : United States. Dept. of Energy. High Energy Physics Division, 2017. and Oak Ridge, Tenn. : Distributed by the Office of Scientific and Technical Information, U.S. Dept. of Energy
- Physical Description:
- pages 892 - 897 : digital, PDF file
- Additional Creators:
- Lawrence Berkeley National Laboratory, United States. Department of Energy. High Energy Physics Division, and United States. Department of Energy. Office of Scientific and Technical Information
- Restrictions on Access:
- Free-to-read Unrestricted online access
- Experiments in particle physics produce enormous quantities of data that must be analyzed and interpreted by teams of physicists. This analysis is often exploratory, where scientists are unable to enumerate the possible types of signal prior to performing the experiment. Thus, tools for summarizing, clustering, visualizing and classifying high-dimensional data are essential. Here in this work, we show that meaningful physical content can be revealed by transforming the raw data into a learned high-level representation using deep neural networks, with measurements taken at the Daya Bay Neutrino Experiment as a case study. We further show how convolutional deep neural networks can provide an effective classification filter with greater than 97% accuracy across different classes of physics events, significantly better than other machine learning approaches.
- Published through SciTech Connect., 02/02/2017., "ark:/13030/qt9dk2b6fm", 2016 15th IEEE International Conference on Machine Learning and Applications (ICMLA) AM, 15. IEEE International Conference on Machine Learning and Applications (ICMLA) , Anaheim, CA (United States), 18-20 Dec 2016., and Evan Racah; Seyoon Ko; Peter Sadowski; Wahid Bhimji; Craig Tull; Sang-Yun Oh; Pierre Baldi; . Prabhat.
- Funding Information:
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