Equivariant flow-based sampling for lattice gauge theory

2020/03/13 by Gurtej Kanwar, Michael S. Albergo, Denis Boyda +5 · 4 citations
Physics and Astronomy · Computer Science · #hep-lat #cond-mat.stat-mech #cs.LG

paper · pdf · doi:10.1103/physrevlett.125.121601

published as Phys. Rev. Lett. 125, 121601 (2020) · 6 pages, 4 figures

arxiv created 2020/03/13 · arxiv updated 2020/09/23

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 near critical points in parameter space 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.

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