Machine learning for four-dimensional SU(3) lattice gauge theories
For lattice gauge theorists, this review consolidates ML approaches to improve sampling and presents scaling results for a novel action, though it is primarily a survey with incremental contributions.
This review covers machine learning methods for sampling gauge field configurations in four-dimensional SU(3) lattice gauge theories, including generative models and renormalization group-improved actions. Scaling results for a machine-learned fixed-point action show continuum-limit behavior with observables free of tree-level lattice artefacts.
In this review I summarize how machine learning can be used in lattice gauge theory simulations and what ap\-proaches are currently available to improve the sampling of gauge field configurations, with a focus on applications in four-dimensional SU(3) gauge theories. These include approaches based on generative machine-learning models such as (stochastic) normalizing flows and diffusion processes, and an approach based on renormalization group (RG) transformations, more specifically the machine learning of RG-improved gauge actions using gauge-equivariant convolutional neural networks. In particular, I present scaling results for a machine-learned fixed-point action in four-dimensional SU(3) gauge theory towards the continuum limit. The results include observables based on the classically perfect gradient-flow scales, which are free of tree-level lattice artefacts to all orders, and quantities related to the static potential and the deconfinement transition.