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Equivariant Flow-Based Sampling for Lattice Gauge Theory
Physical Review Letters ( IF 8.385 ) Pub Date : 2020-09-15 , DOI: 10.1103/physrevlett.125.121601
Gurtej Kanwar; Michael S. Albergo; Denis Boyda; Kyle Cranmer; Daniel C. Hackett; Sébastien Racanière; Danilo Jimenez Rezende; Phiala E. Shanahan

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.
更新日期:2020-09-15

 

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