Sampling using $SU(N)$ gauge equivariant flows

Phys. Rev. D 103, 074504 (2021) We develop a flow-based sampling algorithm for $SU(N)$ lattice gauge theories that is gauge-invariant by construction. Our key contribution is constructing a class of flows on an $SU(N)$ variable (or on a $U(N)$ variable by a simple alternative) that respect matrix co...

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Bibliographic Details
Main Authors Boyda, Denis, Kanwar, Gurtej, Racanière, Sébastien, Rezende, Danilo Jimenez, Albergo, Michael S, Cranmer, Kyle, Hackett, Daniel C, Shanahan, Phiala E
Format Journal Article
LanguageEnglish
Published 12.08.2020
Subjects
Computer Science - Learning
Physics - High Energy Physics - Lattice
Statistics - Machine Learning
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Summary:Phys. Rev. D 103, 074504 (2021) We develop a flow-based sampling algorithm for $SU(N)$ lattice gauge theories that is gauge-invariant by construction. Our key contribution is constructing a class of flows on an $SU(N)$ variable (or on a $U(N)$ variable by a simple alternative) that respect matrix conjugation symmetry. We apply this technique to sample distributions of single $SU(N)$ variables and to construct flow-based samplers for $SU(2)$ and $SU(3)$ lattice gauge theory in two dimensions.
Bibliography:MIT-CTP/5228
DOI:10.48550/arxiv.2008.05456