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High energy nuclear physics meets Machine Learning

Authors :
He, Wan-Bing
Ma, Yu-Gang
Pang, Long-Gang
Song, Huichao
Zhou, Kai
Publication Year :
2023

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.<br />Comment: 30 pages, 20 figures, mini-review

Details

Database :
arXiv
Publication Type :
Report
Accession number :
edsarx.2303.06752
Document Type :
Working Paper