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Title: Equivariant Flow-Based Sampling for Lattice Gauge Theory

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.

Authors:
ORCiD logo; ORCiD logo; ORCiD logo; ; ORCiD logo; ; ORCiD logo;
Publication Date:
Research Org.:
Massachusetts Inst. of Technology (MIT), Cambridge, MA (United States); Argonne National Lab. (ANL), Argonne, IL (United States). Argonne Leadership Computing Facility (ALCF)
Sponsoring Org.:
USDOE Office of Science (SC), Nuclear Physics (NP); National Science Foundation (NSF)
OSTI Identifier:
1661702
Alternate Identifier(s):
OSTI ID: 1713189
Grant/Contract Number:  
SC0011090; AC02-06CH11357; 1841699; ACI-1450310; OAC-1836650; OAC-1841471
Resource Type:
Published Article
Journal Name:
Physical Review Letters
Additional Journal Information:
Journal Name: Physical Review Letters Journal Volume: 125 Journal Issue: 12; Journal ID: ISSN 0031-9007
Publisher:
American Physical Society (APS)
Country of Publication:
United States
Language:
English
Subject:
72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS; lattice QCD; gauge symmetries; lattice field theory; lattice gauge theory; lattice models in condensed matter

Citation Formats

Kanwar, Gurtej, Albergo, Michael S., Boyda, Denis, Cranmer, Kyle, Hackett, Daniel C., Racanière, Sébastien, Rezende, Danilo Jimenez, and Shanahan, Phiala E. Equivariant Flow-Based Sampling for Lattice Gauge Theory. United States: N. p., 2020. Web. https://doi.org/10.1103/physrevlett.125.121601.
Kanwar, Gurtej, Albergo, Michael S., Boyda, Denis, Cranmer, Kyle, Hackett, Daniel C., Racanière, Sébastien, Rezende, Danilo Jimenez, & Shanahan, Phiala E. Equivariant Flow-Based Sampling for Lattice Gauge Theory. United States. https://doi.org/10.1103/physrevlett.125.121601
Kanwar, Gurtej, Albergo, Michael S., Boyda, Denis, Cranmer, Kyle, Hackett, Daniel C., Racanière, Sébastien, Rezende, Danilo Jimenez, and Shanahan, Phiala E. Tue . "Equivariant Flow-Based Sampling for Lattice Gauge Theory". United States. https://doi.org/10.1103/physrevlett.125.121601.
@article{osti_1661702,
title = {Equivariant Flow-Based Sampling for Lattice Gauge Theory},
author = {Kanwar, Gurtej and Albergo, Michael S. and Boyda, Denis and Cranmer, Kyle and Hackett, Daniel C. and Racanière, Sébastien and Rezende, Danilo Jimenez and Shanahan, Phiala E.},
abstractNote = {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.},
doi = {10.1103/physrevlett.125.121601},
journal = {Physical Review Letters},
number = 12,
volume = 125,
place = {United States},
year = {2020},
month = {9}
}

Journal Article:
Free Publicly Available Full Text
Publisher's Version of Record
https://doi.org/10.1103/physrevlett.125.121601

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