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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 this...

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Main Authors: Albergo, Michael S. (Author) , Kanwar, Gurtej (Author) , Racanière, Sébastien (Author) , Rezende, Danilo Jimenez (Author) , Urban, Julian M. (Author) , Boyda, Denis (Author) , Cranmer, Kyle (Author) , Hackett, Daniel C. (Author) , Shanahan, Phiala E. (Author)
Format: Article (Journal)
Language:English
Published: 15 December 2021
In: Physical review
Year: 2021, Volume: 104, Issue: 11, Pages: 1-25
ISSN:2470-0029
DOI:10.1103/PhysRevD.104.114507
Online Access:Verlag, kostenfrei, Volltext: https://doi.org/10.1103/PhysRevD.104.114507
Verlag, kostenfrei, Volltext: https://link.aps.org/doi/10.1103/PhysRevD.104.114507
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Author Notes:Michael S. Albergo, Gurtej Kanwar, Sébastien Racanière, Danilo J. Rezende, Julian M. Urban, Denis Boyda, Kyle Cranmer, Daniel C. Hackett, Phiala E. Shanahan
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Summary: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.
Item Description:Gesehen am 23.06.2022
Physical Description:Online Resource
ISSN:2470-0029
DOI:10.1103/PhysRevD.104.114507

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