Equivariant Flow-Based Sampling for Lattice Gauge Theory
Gurtej Kanwar, Michael S. Albergo, Denis Boyda, K. Cranmer, Daniel C. Hackett, Sébastien Racanière, Danilo Jimenez Rezende, Phiala E. Shanahan
Abstract
We define a class of machine-learned flow-based sampling algorithms for lattice gauge theories that are gauge invariant by construction. We demonstrate the application of this framework to U(1) gauge theory in two spacetime dimensions, and find that, at small bare coupling, the approach is orders of magnitude more efficient at sampling topological quantities than more traditional sampling procedures such as hybrid Monte Carlo and heat bath.
Topics & Concepts
Lattice gauge theoryEquivariant mapLattice field theoryGauge theoryLattice (music)Hamiltonian lattice gauge theoryMonte Carlo methodPhysicsTheoretical physicsSampling (signal processing)Statistical physicsMathematicsParticle physicsPure mathematicsStatisticsDetectorAcousticsOpticsTheoretical and Computational PhysicsTopological and Geometric Data AnalysisAdvanced Data Storage Technologies