Topological defects and confinement with machine learning: the case of monopoles in compact electrodynamics

TL;DR

This paper demonstrates that machine learning can effectively identify phase transitions and predict critical points in a topological quantum field theory by analyzing monopole configurations across different lattice sizes.

Contribution

It introduces a neural network approach to recognize confinement phases and estimate transition points in compact U(1) gauge theory using monopole configurations as input.

Findings

Neural network accurately determines the deconfinement transition temperature.

Model trained on one lattice size generalizes well to others for critical temperature estimation.

Supervised learning on monopole configurations can predict phase transition points in quantum field theories.

Abstract

We investigate the advantages of machine learning techniques to recognize the dynamics of topological objects in quantum field theories. We consider the compact U(1) gauge theory in three spacetime dimensions as the simplest example of a theory that exhibits confinement and mass gap phenomena generated by monopoles. We train a neural network with a generated set of monopole configurations to distinguish between confinement and deconfinement phases, from which it is possible to determine the deconfinement transition point and to predict several observables. The model uses a supervised learning approach and treats the monopole configurations as three-dimensional images (holograms). We show that the model can determine the transition temperature with accuracy, which depends on the criteria implemented in the algorithm. More importantly, we train the neural network with configurations from a single lattice size before making predictions for configurations from other lattice sizes, from which a reliable estimation of the critical temperatures are obtained.