Sampling using SU(N) gauge equivariant flows

被引：70

作者：

Boyda, Denis ^{[1
]}

Kanwar, Gurtej ^{[1
]}

Racaniere, Sebastien ^{[2
]}

Rezende, Danilo Jimenez ^{[2
]}

Albergo, Michael S. ^{[3
]}

Cranmer, Kyle ^{[3
]}

Hackett, Daniel C. ^{[1
]}

Shanahan, Phiala E. ^{[1
]}

机构：

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

[2] DeepMind, London N1C 4AG, England

[3] NYU, Ctr Cosmol & Particle Phys, 550 1St Ave, New York, NY 10003 USA

来源：

PHYSICAL REVIEW D | 2021年 / 103卷 / 07期

基金：

美国国家科学基金会;

关键词：

D O I：

10.1103/PhysRevD.103.074504

中图分类号：

P1 [天文学];

学科分类号：

0704 ;

摘要：

We develop a flow-based sampling algorithm for SU(N) lattice gauge theories that is gauge invariant by construction. Our key contribution is constructing a class of flows on an SU(N) variable [or on a U(N) variable by a simple alternative] that respects matrix conjugation symmetry. We apply this technique to sample distributions of single SU(N) variables and to construct flow-based samplers for SU(2) and SU(3) lattice gauge theory in two dimensions.

引用

页数：25

共 74 条

[1] Flow-based generative models for Markov chain Monte Carlo in lattice field theory [J].