| dc.contributor.author | Kanwar, Gurtej | |
| dc.contributor.author | Albergo, Michael S | |
| dc.contributor.author | Boyda, Denis | |
| dc.contributor.author | Cranmer, Kyle | |
| dc.contributor.author | Hackett, Daniel C | |
| dc.contributor.author | Racanière, Sébastien | |
| dc.contributor.author | Rezende, Danilo Jimenez | |
| dc.contributor.author | Shanahan, Phiala E | |
| dc.date.accessioned | 2021-10-27T20:04:50Z | |
| dc.date.available | 2021-10-27T20:04:50Z | |
| dc.date.issued | 2020 | |
| dc.identifier.uri | https://hdl.handle.net/1721.1/134400 | |
| dc.description.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. | |
| dc.language.iso | en | |
| dc.publisher | American Physical Society (APS) | |
| dc.relation.isversionof | 10.1103/PhysRevLett.125.121601 | |
| dc.rights | Creative Commons Attribution 4.0 International license | |
| dc.rights.uri | https://creativecommons.org/licenses/by/4.0/ | |
| dc.source | APS | |
| dc.title | Equivariant Flow-Based Sampling for Lattice Gauge Theory | |
| dc.type | Article | |
| dc.contributor.department | Massachusetts Institute of Technology. Center for Theoretical Physics | |
| dc.relation.journal | Physical Review Letters | |
| dc.eprint.version | Final published version | |
| dc.type.uri | http://purl.org/eprint/type/JournalArticle | |
| eprint.status | http://purl.org/eprint/status/PeerReviewed | |
| dc.date.updated | 2021-07-09T12:05:49Z | |
| dspace.orderedauthors | Kanwar, G; Albergo, MS; Boyda, D; Cranmer, K; Hackett, DC; Racanière, S; Rezende, DJ; Shanahan, PE | |
| dspace.date.submission | 2021-07-09T12:05:50Z | |
| mit.journal.volume | 125 | |
| mit.journal.issue | 12 | |
| mit.license | PUBLISHER_CC | |
| mit.metadata.status | Authority Work and Publication Information Needed | |
