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arXiv 2106.05934hep-latcond-mat.stat-mechcs.LG
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费米子格点场论的流基采样

Flow-based sampling for fermionic lattice field theories

Center for Cosmology and Particle Physics, New York University(纽约大学宇宙学与粒子物理中心);Center for Theoretical Physics, Massachusetts Institute of Technology(麻省理工学院理论物理中心);DeepMind;Institut für Theoretische Physik, Universität Heidelberg(海德堡大学理论物理研究所);Argonne Leadership Computing Facility, Argonne National Laboratory(阿贡国家实验室阿贡领导力计算设施)

Michael S. Albergo, Gurtej Kanwar, Sébastien Racanière, Danilo J. Rezende, Julian M. Urban, Denis Boyda, Kyle Cranmer, Daniel C. Hackett, Phiala E. Shanahan

摘要:

基于规范化流的算法正在成为有前途的机器学习方法,可以以渐近精确的方式对复杂的概率分布进行采样。在格点场论的背景下,原理证明研究已经证明了这种方法对标量理论,规范理论和统计系统的有效性。这项工作开发的方法,使流为基础的采样理论与动态费米子,这是必要的技术被应用到晶格场论研究的粒子物理学和许多凝聚态系统的标准模型。作为一个实际的演示,这些方法被应用到采样的场位形的二维理论的无质量交错费米子耦合到一个标量场通过汤川相互作用。

英文摘要:

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.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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