Equivariant Flow-Based Sampling for Lattice Gauge Theory
Name
PhysRevLett.125.121601.pdf
Description
Published version
Size
529.21 KB
Format
Adobe PDF
Checksum (MD5)
ac7a1ea171b0db761c360d4790bc308d
Author(s)
Kanwar, Gurtej
Albergo, Michael S
Boyda, Denis
Cranmer, Kyle
Hackett, Daniel C
Racanière, Sébastien
Rezende, Danilo Jimenez
Shanahan, Phiala E
Date Issued
2020
Journal
Physical Review Letters
Publisher
American Physical Society (APS)
Version
Final published version
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.
MIT Department
Massachusetts Institute of Technology. Center for Theoretical Physics
Terms of Use
Creative Commons Attribution 4.0 International license
Persistent DSpace Link
DOI of Published Version
https://doi.org/10.1103/PhysRevLett.125.121601
