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High energy nuclear physics meets Machine Learning

High Energy Physics - Phenomenology 2023-03-14 v1 High Energy Physics - Experiment Nuclear Experiment Nuclear Theory

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.

Keywords

Cite

@article{arxiv.2303.06752,
  title  = {High energy nuclear physics meets Machine Learning},
  author = {Wan-Bing He and Yu-Gang Ma and Long-Gang Pang and Huichao Song and Kai Zhou},
  journal= {arXiv preprint arXiv:2303.06752},
  year   = {2023}
}

Comments

30 pages, 20 figures, mini-review

R2 v1 2026-06-28T09:13:08.011Z