Flow-based sampling for fermionic lattice field theories
Name
PhysRevD.104.114507.pdf
Description
Published version
Size
2.17 MB
Format
Adobe PDF
Checksum (MD5)
3806b95e9c65233e2d3312eb4c45f7d8
Author(s)
Albergo, Michael S
Kanwar, Gurtej
Racanière, Sébastien
Rezende, Danilo J
Urban, Julian M
Boyda, Denis
Cranmer, Kyle
Hackett, Daniel C
Shanahan, Phiala E
Date Issued
2021
Journal
Physical Review D
Publisher
American Physical Society (APS)
Citation
Albergo, Michael S, Kanwar, Gurtej, Racanière, Sébastien, Rezende, Danilo J, Urban, Julian M et al. 2021. "Flow-based sampling for fermionic lattice field theories." Physical Review D, 104 (11).
Version
Final published version
Abstract
Algorithms based on normalizing flows are emerging as promising machine
learning approaches to sampling complicated probability distributions in a way
that can be made asymptotically exact. In the context of lattice field theory,
proof-of-principle studies have demonstrated the effectiveness of this approach
for scalar theories, gauge theories, and statistical systems. This work
develops approaches that enable flow-based sampling of theories with dynamical
fermions, which is necessary for the technique to be applied to lattice field
theory studies of the Standard Model of particle physics and many condensed
matter systems. As a practical demonstration, these methods are applied to the
sampling of field configurations for a two-dimensional theory of massless
staggered fermions coupled to a scalar field via a Yukawa interaction.
MIT Department
Massachusetts Institute of Technology. Center for Theoretical Physics
Massachusetts Institute of Technology. Department of Physics
Terms of Use
Creative Commons Attribution 4.0 International License
Persistent DSpace Link
DOI of Published Version
https://doi.org/10.1103/PHYSREVD.104.114507
