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

Understand

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).