Equivariant Flow-Based Sampling for Lattice Gauge Theory

Author(s)
Kanwar, Gurtej; Albergo, Michael S; Boyda, Denis; Cranmer, Kyle; Hackett, Daniel C; ... Show more
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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.
Date issued
2020
URI
https://hdl.handle.net/1721.1/134400
Department
Massachusetts Institute of Technology. Center for Theoretical Physics
Journal
Physical Review Letters
Publisher
American Physical Society (APS)
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