Equivariant Flow-Based Sampling for Lattice Gauge Theory

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, at small bare coupling, the approach is orders of...

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Bibliographic Details
Main Authors: Kanwar, Gurtej, Albergo, Michael S, Boyda, Denis, Cranmer, Kyle, Hackett, Daniel C, Racanière, Sébastien, Rezende, Danilo Jimenez, Shanahan, Phiala E
Other Authors: Massachusetts Institute of Technology. Center for Theoretical Physics
Format: Article
Language:English
Published: American Physical Society (APS) 2021
Online Access:https://hdl.handle.net/1721.1/134400

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