用梯度优化方法自动寻找最优规范固定方案，提升物理量计算精度。
Exploring gauge-fixing conditions with gradient-based optimization
- 提出可微分的规范固定参数化框架，覆盖朗道、库伦和最大树等规范
- 通过梯度下降优化目标损失函数，自动选择最优规范条件
- 适用于需要高精度规范相关量计算的研究者，如重整化与模型对比
格点规范固定是计算规范变量子的关键步骤，例如在RI-MOM重整化方案或模型计算对比中。近期发现，结合轮廓变形后，规范变量子更易于实现信噪比优化。这推动了对规范固定方案的系统性参数化与探索。本文提出一种可微分的规范固定参数化方法，覆盖朗道规范、库伦规范及最大树规范。利用伴随状态法，可基于梯度优化，选择使任意目标损失函数最小的规范固定方案。
原文摘要 · Abstract (English)
Lattice gauge fixing is required to compute gauge-variant quantities, for example those used in RI-MOM renormalization schemes or as objects of comparison for model calculations. Recently, gauge-variant quantities have also been found to be more amenable to signal-to-noise optimization using contour deformations. These applications motivate systematic parameterization and exploration of gauge-fixing schemes. This work introduces a differentiable parameterization of gauge fixing which is broad enough to cover Landau gauge, Coulomb gauge, and maximal tree gauges. The adjoint state method allows gradient-based optimization to select gauge-fixing schemes that minimize an arbitrary target loss function.
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