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
TL;DR
This paper reviews how machine learning techniques are transforming nuclear physics research, enabling new scientific discoveries and societal applications across diverse topics.
Contribution
It provides a comprehensive overview of the application of machine learning in nuclear physics, highlighting recent advances and potential future impacts.
Findings
Machine learning accelerates data analysis in nuclear experiments.
New models improve predictive accuracy in nuclear phenomena.
Integration of ML techniques fosters interdisciplinary research.
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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