Colloquium : Machine learning in nuclear physics

A. Boehnlein; M. Diefenthaler; Nobuo Sato; M. Schram; V. Ziegler; C. Fanelli; Morten Hjorth-Jensen; Tanja Horn; Michelle Kuchera; Dean Lee; Witold Nazarewicz; Peter N. Ostroumov; Kostas Orginos; Alan W. P. Poon; Xin-Nian Wang; Alexander Scheinker; Michael Scott Smith; Long-Gang Pang

doi:10.1103/revmodphys.94.031003

Colloquium : Machine learning in nuclear physics

A. Boehnlein (Thomas Jefferson National Accelerator Facility) iD M. Diefenthaler (Thomas Jefferson National Accelerator Facility) iD Nobuo Sato (Thomas Jefferson National Accelerator Facility) iD M. Schram (Thomas Jefferson National Accelerator Facility) iD V. Ziegler (Thomas Jefferson National Accelerator Facility) C. Fanelli (Thomas Jefferson National Accelerator Facility) iD Morten Hjorth-Jensen (University of Oslo) iD Tanja Horn (Thomas Jefferson National Accelerator Facility) iD Michelle Kuchera (Davidson College) iD Dean Lee (Thomas Jefferson National Accelerator Facility) iD Witold Nazarewicz (Thomas Jefferson National Accelerator Facility) iD Peter N. Ostroumov (Thomas Jefferson National Accelerator Facility) iD Kostas Orginos (William & Mary) iD Alan W. P. Poon (Lawrence Berkeley National Laboratory) iD Xin-Nian Wang (Lawrence Berkeley National Laboratory) iD Alexander Scheinker (Los Alamos National Laboratory) iD Michael Scott Smith (Oak Ridge National Laboratory) iD Long-Gang Pang (Thomas Jefferson National Accelerator Facility) iD

Journal: Reviews of Modern Physics • 2022-09-08 • American Physical Society • ISSN: 0034-6861

Abstract

Nuclear physics deals with complex systems, large datasets, and complicated correlations between parameters, which makes the field suitable for the application of machine learning techniques. Machine learning can help classify and analyze data, find hidden correlations, and assist in the design of new experiments and detectors. This Colloquium explains how this will lead to advances in nuclear theory, experimental methods and data acquisition, and accelerator technology.

Subjects & Keywords

# Nuclear physics research studies # Physics and Astronomy # Machine Learning in Materials Science # Materials Science # Neutrino Physics Research

How to Cite this Article

                    A. Boehnlein, M. Diefenthaler, Nobuo Sato, et al. (2022). "Colloquium : Machine learning in nuclear physics". Reviews of Modern Physics. https://doi.org/10.1103/revmodphys.94.031003                

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Identifiers & Provenance

DOI: https://doi.org/10.1103/revmodphys.94.031003 ↗
OpenAlex: W4295101746 ↗
Primary Index: OPENALEX