Flow-based sampling for fermionic lattice field theories

被引：28

作者：

Albergo, Michael S. ^{[1
]}

Kanwar, Gurtej ^{[2
,3
]}

Racaniere, Sebastien ^{[4
]}

Rezende, Danilo J. ^{[4
]}

Urban, Julian M. ^{[5
]}

Boyda, Denis ^{[2
,3
,6
]}

Cranmer, Kyle ^{[1
]}

Hackett, Daniel C. ^{[2
,3
]}

Shanahan, Phiala E. ^{[2
,3
]}

机构：

[1] NYU, Ctr Cosmol & Particle Phys, New York, NY 10003 USA

[2] MIT, Ctr Theoret Phys, Cambridge, MA 02139 USA

[3] NSF AI Inst Artificial Intelligence & Fundamental, Cambridge, MA 02139 USA

[4] DeepMind, London N1C 4DJ, England

[5] Heidelberg Univ, Inst Theoret Phys, Philosophenweg 16, D-69120 Heidelberg, Germany

[6] Argonne Natl Lab, Argonne Leadership Comp Facil, Lemont, IL 60439 USA

来源：

PHYSICAL REVIEW D | 2021年 / 104卷 / 11期

基金：

美国国家科学基金会;

关键词：

MONTE-CARLO-SIMULATION; DENSITY-ESTIMATION; HMC ALGORITHM; QCD; INTEGRATION; DYNAMICS; MATRIX; QUARK; TRACE;

D O I：

10.1103/PhysRevD.104.114507

中图分类号：

P1 [天文学];

学科分类号：

0704 ;

摘要：

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.

引用

页数：25

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