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Deep Learning in the Natural Sciences: Applications to Physics

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Braverman Readings in Machine Learning. Key Ideas from Inception to Current State

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 11100))

Abstract

Machine learning is increasingly being used not only in engineering applications such as computer vision and speech recognition, but in data analysis for the natural sciences. Here we describe applications of deep learning to four areas of experimental sub-atomic physics — high-energy physics, antimatter physics, neutrino physics, and dark matter physics.

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Authors and Affiliations

  1. Department of Computer Science, University of California, Irvine, Irvine, USA

    Peter Sadowski & Pierre Baldi

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  1. Peter Sadowski
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  2. Pierre Baldi
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Correspondence to Pierre Baldi .

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Editors and Affiliations

  1. West Newton, MA, USA

    Lev Rozonoer

  2. National Research University Higher School of Economics, Moscow, Russia

    Boris Mirkin

  3. Rutgers University, Piscataway, NJ, USA

    Ilya Muchnik

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Sadowski, P., Baldi, P. (2018). Deep Learning in the Natural Sciences: Applications to Physics. In: Rozonoer, L., Mirkin, B., Muchnik, I. (eds) Braverman Readings in Machine Learning. Key Ideas from Inception to Current State. Lecture Notes in Computer Science(), vol 11100. Springer, Cham. https://doi.org/10.1007/978-3-319-99492-5_12

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  • DOI: https://doi.org/10.1007/978-3-319-99492-5_12

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