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

TL;DR

This paper demonstrates that 3D convolutional neural networks can effectively classify phases and identify phase transitions in the 3D Ising model, offering insights relevant to QCD phase studies.

Contribution

It introduces a neural network approach to classify phases and detect phase transitions in the 3D Ising model, aiding understanding of QCD critical phenomena.

Findings

CNN accurately predicts physical quantities in spin configurations.

The model distinguishes both first- and second-order phase transitions.

Features important for phase discrimination are identified.

Abstract

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 effectivelly 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.