Flow-based sampling for fermionic lattice field theories
Flow-based sampling for fermionic lattice field theories
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DOI：
10.1103/physrevd.104.114507
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发表时间：
2021-06
期刊：
影响因子：
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通讯作者：
M. S. Albergo;G. Kanwar;S. Racanière;Danilo Jimenez Rezende;Julian M. Urban;D. Boyda;Kyle Cranmer;D. Hackett;P. Shanahan
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文献类型：
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作者：
M. S. Albergo;G. Kanwar;S. Racanière;Danilo Jimenez Rezende;Julian M. Urban;D. Boyda;Kyle Cranmer;D. Hackett;P. Shanahan
基于流归一化的算法正在成为一种很有前途的机器学习方法，用于以一种可以使其渐近准确的方式对复杂的概率分布进行采样。在格子场理论的背景下，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。这项工作发展了一种方法，使基于流动的采样具有动力学费米子，这是必要的技术，以应用于格子场理论研究的标准模型的粒子物理和许多凝聚态系统。作为一个实际演示，这些方法被应用于无质量交错费米子通过Yukawa相互作用耦合到标量场的二维理论的场组态采样。
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.