Flow-based sampling for fermionic lattice field theories
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 t...
| Main Authors: | , , , , , , , , |
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| Other Authors: | |
| Format: | Article |
| Language: | English |
| Published: |
American Physical Society (APS)
2022
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| Online Access: | https://hdl.handle.net/1721.1/142202 |
| _version_ | 1826198529702363136 |
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| author | Albergo, Michael S Kanwar, Gurtej Racanière, Sébastien Rezende, Danilo J Urban, Julian M Boyda, Denis Cranmer, Kyle Hackett, Daniel C Shanahan, Phiala E |
| author2 | Massachusetts Institute of Technology. Center for Theoretical Physics |
| author_facet | Massachusetts Institute of Technology. Center for Theoretical Physics Albergo, Michael S Kanwar, Gurtej Racanière, Sébastien Rezende, Danilo J Urban, Julian M Boyda, Denis Cranmer, Kyle Hackett, Daniel C Shanahan, Phiala E |
| author_sort | Albergo, Michael S |
| collection | MIT |
| description | 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. |
| first_indexed | 2024-09-23T11:06:18Z |
| format | Article |
| id | mit-1721.1/142202 |
| institution | Massachusetts Institute of Technology |
| language | English |
| last_indexed | 2024-09-23T11:06:18Z |
| publishDate | 2022 |
| publisher | American Physical Society (APS) |
| record_format | dspace |
| spelling | mit-1721.1/1422022023-07-28T20:42:34Z Flow-based sampling for fermionic lattice field theories Albergo, Michael S Kanwar, Gurtej Racanière, Sébastien Rezende, Danilo J Urban, Julian M Boyda, Denis Cranmer, Kyle Hackett, Daniel C Shanahan, Phiala E Massachusetts Institute of Technology. Center for Theoretical Physics Massachusetts Institute of Technology. Department of Physics 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. 2022-04-29T16:18:21Z 2022-04-29T16:18:21Z 2021 2022-04-29T16:11:37Z Article http://purl.org/eprint/type/JournalArticle https://hdl.handle.net/1721.1/142202 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). en 10.1103/PHYSREVD.104.114507 Physical Review D Creative Commons Attribution 4.0 International License https://creativecommons.org/licenses/by/4.0 application/pdf American Physical Society (APS) APS |
| spellingShingle | Albergo, Michael S Kanwar, Gurtej Racanière, Sébastien Rezende, Danilo J Urban, Julian M Boyda, Denis Cranmer, Kyle Hackett, Daniel C Shanahan, Phiala E Flow-based sampling for fermionic lattice field theories |
| title | Flow-based sampling for fermionic lattice field theories |
| title_full | Flow-based sampling for fermionic lattice field theories |
| title_fullStr | Flow-based sampling for fermionic lattice field theories |
| title_full_unstemmed | Flow-based sampling for fermionic lattice field theories |
| title_short | Flow-based sampling for fermionic lattice field theories |
| title_sort | flow based sampling for fermionic lattice field theories |
| url | https://hdl.handle.net/1721.1/142202 |
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