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...
| Main Authors: | , , , , , , , |
|---|---|
| Other Authors: | |
| Format: | Article |
| Language: | English |
| Published: |
American Physical Society (APS)
2021
|
| Online Access: | https://hdl.handle.net/1721.1/134400 |
| _version_ | 1826211761090461696 |
|---|---|
| author | Kanwar, Gurtej Albergo, Michael S Boyda, Denis Cranmer, Kyle Hackett, Daniel C Racanière, Sébastien Rezende, Danilo Jimenez Shanahan, Phiala E |
| author2 | Massachusetts Institute of Technology. Center for Theoretical Physics |
| author_facet | Massachusetts Institute of Technology. Center for Theoretical Physics Kanwar, Gurtej Albergo, Michael S Boyda, Denis Cranmer, Kyle Hackett, Daniel C Racanière, Sébastien Rezende, Danilo Jimenez Shanahan, Phiala E |
| author_sort | Kanwar, Gurtej |
| collection | MIT |
| description | 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. |
| first_indexed | 2024-09-23T15:11:01Z |
| format | Article |
| id | mit-1721.1/134400 |
| institution | Massachusetts Institute of Technology |
| language | English |
| last_indexed | 2024-09-23T15:11:01Z |
| publishDate | 2021 |
| publisher | American Physical Society (APS) |
| record_format | dspace |
| spelling | mit-1721.1/1344002023-02-17T21:21:33Z Equivariant Flow-Based Sampling for Lattice Gauge Theory Kanwar, Gurtej Albergo, Michael S Boyda, Denis Cranmer, Kyle Hackett, Daniel C Racanière, Sébastien Rezende, Danilo Jimenez Shanahan, Phiala E Massachusetts Institute of Technology. Center for Theoretical Physics 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. 2021-10-27T20:04:50Z 2021-10-27T20:04:50Z 2020 2021-07-09T12:05:49Z Article http://purl.org/eprint/type/JournalArticle https://hdl.handle.net/1721.1/134400 en 10.1103/PhysRevLett.125.121601 Physical Review Letters Creative Commons Attribution 4.0 International license https://creativecommons.org/licenses/by/4.0/ application/pdf American Physical Society (APS) APS |
| spellingShingle | 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 |
| title | Equivariant Flow-Based Sampling for Lattice Gauge Theory |
| title_full | Equivariant Flow-Based Sampling for Lattice Gauge Theory |
| title_fullStr | Equivariant Flow-Based Sampling for Lattice Gauge Theory |
| title_full_unstemmed | Equivariant Flow-Based Sampling for Lattice Gauge Theory |
| title_short | Equivariant Flow-Based Sampling for Lattice Gauge Theory |
| title_sort | equivariant flow based sampling for lattice gauge theory |
| url | https://hdl.handle.net/1721.1/134400 |
| work_keys_str_mv | AT kanwargurtej equivariantflowbasedsamplingforlatticegaugetheory AT albergomichaels equivariantflowbasedsamplingforlatticegaugetheory AT boydadenis equivariantflowbasedsamplingforlatticegaugetheory AT cranmerkyle equivariantflowbasedsamplingforlatticegaugetheory AT hackettdanielc equivariantflowbasedsamplingforlatticegaugetheory AT racanieresebastien equivariantflowbasedsamplingforlatticegaugetheory AT rezendedanilojimenez equivariantflowbasedsamplingforlatticegaugetheory AT shanahanphialae equivariantflowbasedsamplingforlatticegaugetheory |