Flow-based sampling for fermionic lattice field theories

• Verfasst von: Albergo, Michael S. [VerfasserIn]
• Verfasst von: Kanwar, Gurtej [VerfasserIn]
• Verfasst von: Racanière, Sébastien [VerfasserIn]
• Verfasst von: Rezende, Danilo Jimenez [VerfasserIn]
• Verfasst von: Urban, Julian M. [VerfasserIn]
• Verfasst von: Boyda, Denis [VerfasserIn]
• Verfasst von: Cranmer, Kyle [VerfasserIn]
• Verfasst von: Hackett, Daniel C. [VerfasserIn]
• Verfasst von: Shanahan, Phiala E. [VerfasserIn]
• Titel: Flow-based sampling for fermionic lattice field theories
• Verf.angabe: Michael S. Albergo, Gurtej Kanwar, Sébastien Racanière, Danilo J. Rezende, Julian M. Urban, Denis Boyda, Kyle Cranmer, Daniel C. Hackett, Phiala E. Shanahan
• E-Jahr: 2021
• Jahr: 15 December 2021
• Umfang: 25 S.
• Fussnoten: Gesehen am 23.06.2022
• Titel Quelle: Enthalten in: Physical review
• Ort Quelle: Woodbury, NY : Inst., 2016
• Jahr Quelle: 2021
• Band/Heft Quelle: 104(2021), 11, Artikel-ID 114507, Seite 1-25
• ISSN Quelle: 2470-0029
• DOI: doi:10.1103/PhysRevD.104.114507
• Datenträger: Online-Ressource
• Sprache: eng
• K10plus-PPN: 1807583627

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.