Equivariant flow-based sampling for lattice gauge theory

• URL: http://arxiv.org/abs/2003.06413v1
• Date: Fri, 13 Mar 2020 17:54:05 GMT
• Title: Equivariant flow-based sampling for lattice gauge theory
• Authors: Gurtej Kanwar, Michael S. Albergo, Denis Boyda, Kyle Cranmer, Daniel C. Hackett, S\'ebastien Racani\`ere, Danilo Jimenez Rezende, Phiala E. Shanahan
• Abstract summary: We demonstrate the application of this framework to U(1) gauge theory in two spacetime dimensions. We 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.
• Score: 10.163463390007617
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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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