Machine learning phase transitions of the three-dimensional Ising universality class  

作　　者：李笑冰 郭冉冉 周宇 刘康宁 赵佳 龙芬 吴元芳 李治明 Xiaobing Li;Ranran Guo;Yu Zhou;Kangning Liu;Jia Zhao;Fen Long;Yuanfang Wu;Zhiming Li(Key Laboratory of Quark and Lepton Physics(MOE)and Institute of Particle Physics,Central China Normal University,Wuhan 430079,China;University of California,Los Angeles,CA 90095,USA)

机构地区：[1]Key Laboratory of Quark and Lepton Physics(MOE)and Institute of Particle Physics,Central China Normal University,Wuhan 430079,China [2]University of California,Los Angeles,CA 90095,USA

出　　处：《Chinese Physics C》2023年第3期138-145,共8页中国物理C（英文版）

基　　金：Supported by the National Natural Science Foundation of China(12275102);the National Key Research and Development Program of China(2022YFA1604900)。

摘　　要：Exploration of the QCD phase diagram and critical point is one of the main goals in current relativistic heavy-ion collisions.The QCD critical point is expected to belong to a three-dimensional(3D)Ising universality class.Machine learning techniques are found to be powerful in distinguishing different phases of matter and provide a new way to study the phase diagram.We investigate phase transitions in the 3D cubic Ising model using supervised learning methods.It is found that a 3D convolutional neural network can be trained to effectively predict physical quantities in different spin configurations.With a uniform neural network architecture,it can encode phases of matter and identify both second-and first-order phase transitions.The important features that discriminate different phases in the classification processes are investigated.These findings can help study and understand QCD phase transitions in relativistic heavy-ion collisions.

关 键 词：machine learning phase transition QCD critical point three-dimensional Ising universality class 

分 类 号：O572.243[理学—粒子物理与原子核物理] TP181[理学—物理]