<i>Colloquium</i>: Machine learning in nuclear physics
A. Boehnlein, M. Diefenthaler, N. Sato, Malachi Schram, V. Ziegler, C. Fanelli, M. Hjorth‐Jensen, T. Horn, Michelle Kuchera, Dean Lee, W. Nazarewicz, P. N. Ostroumov, Kostas Orginos, A. W. P. Poon, Xin-Nian Wang, Alexander Scheinker, M. S. Smith, Long-Gang Pang
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.
Topics & Concepts
PhysicsSnapshot (computer storage)Data scienceEngineering ethicsComputer scienceEngineeringOperating systemNuclear reactor physics and engineeringNuclear Physics and ApplicationsNuclear physics research studies