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arXiv · 2112.02309

Machine Learning in Nuclear Physics

Amber Boehnlein·Markus Diefenthaler·Cristiano Fanelli·Morten Hjorth-Jensen·Tanja Horn·Michelle P. Kuchera·Dean Lee·Witold Nazarewicz·Kostas Orginos·Peter Ostroumov·Long-Gang Pang·Alan Poon·Nobuo Sato·Malachi Schram·Alexander Scheinker·Michael S. Smith·Xin-Nian Wang·Veronique Ziegler
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Abstract

Advances in machine learning methods provide tools that have broad applicability in scientific research. These techniques are being applied across the diversity of nuclear physics research topics, leading to advances that will facilitate scientific discoveries and societal applications. This Review gives a snapshot of nuclear physics research which has been transformed by machine learning techniques.

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Amber Boehnlein, Markus Diefenthaler, Cristiano Fanelli, Morten Hjorth-Jensen, Tanja Horn, Michelle P. Kuchera, Dean Lee, Witold Nazarewicz, Kostas Orginos, Peter Ostroumov, Long-Gang Pang, Alan Poon, Nobuo Sato, Malachi Schram, Alexander Scheinker, Michael S. Smith, Xin-Nian Wang, Veronique Ziegler. 2021-12-04. Machine Learning in Nuclear Physics. https://doi.org/10.1103/revmodphys.94.031003

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