Applications of Machine Learning in Relativistic Heavy Ion Physics

Recently, with rapid hardware and algorithms development, machine learning has been widely used as a significant data analysis method. This article reviews the application of different machine learning algorithms in heavy ion collisions, including impact parameter prediction, nuclear deformation parameter prediction, phase transitions classification, fluid evolution simulation, etc. The machine learning algorithms comprise classic machine learning algorithms, such as ensemble learning and principal component analysis, and deep learning algorithms, such as convolutional neural networks and point cloud networks. Because of the excellent performance and efficiency of machine learning, these applications will receive much attention in our field.

Recommended citation: Zhou M, Luo Y, Song H. Applications of machine learning in relativistic heavy ion physics. SCIENTIA SINICA Physica, Mechanica & Astronomica. 2022 Apr 15; 52(5).
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