用于格点规范理论的基于等变流的采样
Equivariant Flow-Based Sampling for Lattice Gauge Theory.
作者信息
Kanwar Gurtej, Albergo Michael S, Boyda Denis, Cranmer Kyle, Hackett Daniel C, Racanière Sébastien, Rezende Danilo Jimenez, Shanahan Phiala E
机构信息
Center for Theoretical Physics, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139, USA.
Center for Cosmology and Particle Physics, New York University, New York, New York 10003, USA.
出版信息
Phys Rev Lett. 2020 Sep 18;125(12):121601. doi: 10.1103/PhysRevLett.125.121601.
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.
我们定义了一类用于格点规范理论的基于机器学习的流采样算法，这类算法在构造上是规范不变的。我们展示了这个框架在二维时空的U(1)规范理论中的应用，并且发现，在小的裸耦合情况下，与诸如混合蒙特卡罗和热浴等更传统的采样程序相比，该方法在采样拓扑量方面效率要高几个数量级。
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