Revealing Fundamental Physics from the Daya Bay Neutrino Experiment Using Deep Neural Networks [electronic resource]

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Tác giả:

Ngôn ngữ: eng

Ký hiệu phân loại: 636.7 Dogs

Thông tin xuất bản: Washington, D.C. : Oak Ridge, Tenn. : United States. Dept. of Energy. Office of High Energy Physics ; Distributed by the Office of Scientific and Technical Information, U.S. Dept. of Energy, 2017

Mô tả vật lý: Size: p. 892 - 897 : , digital, PDF file.

Bộ sưu tập: Metadata

ID: 261200

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.
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