Equivariant flow-based sampling for lattice gauge theory

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 approa...

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Bibliographic Details
Published in:arXiv.org 2020-03
Main Authors: Kanwar, Gurtej, Albergo, Michael S, Boyda, Denis, Cranmer, Kyle, Hackett, Daniel C, Racanière, Sébastien, Danilo Jimenez Rezende, Shanahan, Phiala E
Format: Article
Language:English
Subjects:
Algorithms
Critical point
Gauge theory
Sampling
Online Access:Get full text
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Summary: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.
ISSN:2331-8422
DOI:10.48550/arxiv.2003.06413