用归一化流降低格点量子色动力学观测量方差，提升计算效率。
Variance reduction in lattice QCD observables via normalizing flows
- 通过归一化流构造无偏低方差估计器，用于强相互作用物理量计算。
- 在胶球关联函数和强子结构相关矩阵元中实现10到60倍方差降低。
- 方法对晶格体积不敏感，适合大规模模拟，节省训练成本。
归一化流可用于构建格点场论中依赖于作用量参数导数的物理量的无偏、低方差估计器。本文将该方法应用于四维空间中的SU(3)杨-米尔斯理论及两味量子色动力学（QCD）中的胶子算符插入观测量。在胶球关联函数和与强子结构相关的胶子矩阵元中，方差降低了10至60倍，并展现出显著的计算优势。观察到的方差降低与晶格体积基本无关，因此可通过体积转移策略最小化训练开销。
原文摘要 · Abstract (English)
Normalizing flows can be used to construct unbiased, reduced-variance estimators for lattice field theory observables that are defined by a derivative with respect to action parameters. This work implements the approach for observables involving gluonic operator insertions in the SU(3) Yang-Mills theory and two-flavor Quantum Chromodynamics (QCD) in four space-time dimensions. Variance reduction by factors of $10$-$60$ is achieved in glueball correlation functions and in gluonic matrix elements related to hadron structure, with demonstrated computational advantages. The observed variance reduction is found to be approximately independent of the lattice volume, so that volume transfer can be utilized to minimize training costs.
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