Machine-learned fixed-point actions and observables for SU(3) lattice gauge theory

Lattice regularization is the established approach for studying non-perturbative phenomena in quantum chromodynamics, but accurate predictions for the continuum theory remain challenging because standard actions exhibit large lattice artifacts. The renormalization group on the lattice provides a way of suppressing these artifacts: classically perfect fixed-point (FP) actions. In this talk, I show how gauge-equivariant neural networks yield accurate parametrizations of FP actions. Using these machine-learned actions, we perform Monte Carlo simulations to measure gradient-flow scales with highly suppressed artifacts compared to unimproved actions. I will also present preliminary results for machine-learned FP observables to improve the extraction of the topological susceptibility in four-dimensional SU(3) gauge theory.