用随机归一化流模拟强子间胶子弦的振动形态，提升量子色动力学计算效率。
Stochastic normalizing flows for Effective String Theory
- 结合归一化流与非平衡随机更新，构建可扩展的机器学习采样算法
- 首次在晶格上实现对胶子弦形状的高精度数值模拟
- 适合从事量子场论、机器学习交叉研究的学者参考
有效弦理论（EST）是研究纯规范理论中束缚现象的强大工具，将静止夸克-反夸克对之间的束缚胶子通量管建模为一根细长的振动弦。近年来，基于流的采样器作为高效数值方法被用于在晶格上正则化的有效弦理论研究，为此前难以通过传统解析方法获得的可观测量开辟了新路径。流基采样器是一类基于归一化流（NFs）的深度生成模型，近年来被视为替代传统马尔可夫链蒙特卡洛方法在格点场论计算中的有前景方案。本文将归一化流层与非平衡随机更新相结合，提出随机归一化流（SNFs），一类可基于随机热力学解释的可扩展机器学习算法。我们概述了有效弦理论与SNFs，并报告了关于胶子通量管形状的一些数值结果。
原文摘要 · Abstract (English)
Effective String Theory (EST) is a powerful tool used to study confinement in pure gauge theories by modeling the confining flux tube connecting a static quark-anti-quark pair as a thin vibrating string. Recently, flow-based samplers have been applied as an efficient numerical method to study EST regularized on the lattice, opening the route to study observables previously inaccessible to standard analytical methods. Flow-based samplers are a class of algorithms based on Normalizing Flows (NFs), deep generative models recently proposed as a promising alternative to traditional Markov Chain Monte Carlo methods in lattice field theory calculations. By combining NF layers with out-of-equilibrium stochastic updates, we obtain Stochastic Normalizing Flows (SNFs), a scalable class of machine learning algorithms that can be explained in terms of stochastic thermodynamics. In this contribution, we outline EST and SNFs, and report some numerical results for the shape of the flux tube.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。