High energy nuclear physics meets Machine Learning

TL;DR

This paper reviews how machine learning techniques are increasingly being applied to high energy nuclear physics, highlighting recent progress and potential for future research in this interdisciplinary area.

Contribution

It provides an overview of current applications of machine learning in high energy nuclear physics, emphasizing the growing integration and potential benefits.

Findings

Machine learning is actively used to analyze complex nuclear physics data.

Recent studies demonstrate improved data interpretation with ML methods.

The review encourages further adoption of ML in the field.

Abstract

Though being seemingly disparate and with relatively new intersection, high energy nuclear physics and machine learning have already begun to merge and yield interesting results during the last few years. It's worthy to raise the profile of utilizing this novel mindset from machine learning in high energy nuclear physics, to help more interested readers see the breadth of activities around this intersection. The aim of this mini-review is to introduce to the community the current status and report an overview of applying machine learning for high energy nuclear physics, to present from different aspects and examples how scientific questions involved in high energy nuclear physics can be tackled using machine learning.