Machine Learning in Nuclear Physics
Nuclear Theory
2022-09-21 v2 Machine Learning
High Energy Physics - Experiment
Nuclear Experiment
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.
Keywords
Cite
@article{arxiv.2112.02309,
title = {Machine Learning in Nuclear Physics},
author = {Amber Boehnlein and Markus Diefenthaler and Cristiano Fanelli and Morten Hjorth-Jensen and Tanja Horn and Michelle P. Kuchera and Dean Lee and Witold Nazarewicz and Kostas Orginos and Peter Ostroumov and Long-Gang Pang and Alan Poon and Nobuo Sato and Malachi Schram and Alexander Scheinker and Michael S. Smith and Xin-Nian Wang and Veronique Ziegler},
journal= {arXiv preprint arXiv:2112.02309},
year = {2022}
}
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