Review of Deep Learning in High-Energy Heavy-Ion Collisions

The hot deconfined matter called quark–gluon plasma (QGP) can be generated in relativistic heavy-ion collisions (HICs). Its properties under high temperatures have been widely studied. Since the short-lived QGP is not directly observable, data-driven methods, including deep learning, are often used to infer the initial-state properties from the final distributions of hadrons. This paper reviews various applications of machine learning in relativistic heavy-ion collisions, explains the fundamental concepts of deep learning, and discusses how the properties of HIC data can be interpreted using efficient machine learning models.