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DOE OSTI · 1886246

Colloquium : Machine learning in nuclear physics

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

We report 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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73 NUCLEAR PHYSICS AND RADIATION PHYSICSmachine learningnuclear binding

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Boehnlein, Amber, Diefenthaler, Markus, Sato, Nobuo, Schram, Malachi, Ziegler, Veronique, Fanelli, Cristiano, Hjorth-Jensen, Morten, Horn, Tanja, Kuchera, Michelle P., Lee, Dean, Nazarewicz, Witold, Ostroumov, Peter, Orginos, Kostas, Poon, Alan, Wang, Xin-Nian, Scheinker, Alexander, Smith, Michael S., Pang, Long-Gang. 2022-09-08. Colloquium : Machine learning in nuclear physics. https://doi.org/10.1103/revmodphys.94.031003

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