Machine learning in nuclear physics at low and intermediate energies

Abstract

Machine learning (ML) is becoming a new paradigm for scientific research in various research fields due to its exciting and powerful capability of modeling tools used for big-data processing tasks. In this review, we first briefly introduce the different methodologies used in ML algorithms and techniques. As a snapshot of many applications by ML, some selected applications are presented, especially for low- and intermediate-energy nuclear physics, which include topics on theoretical applications in nuclear structure, nuclear reactions, properties of nuclear matter, and experimental applications in event identification/reconstruction, complex system control, and firmware performance. Finally, we present a summary and outlook on the possible directions of ML use in low-intermediate energy nuclear physics and possible improvements in ML algorithms.