学习格点量子色动力学中夸克传播子的低阶近似
Learning Low-Order Approximations of the Quark Propagator in Lattice QCD
- University of Regensburg（雷根斯堡大学）
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
本文提出用克利福德基下的规范等变神经网络学习格点QCD中夸克传播子的低阶近似，优于跳跃展开并发现新级数，可推广至新规范场和体积。
AI中文摘要：
跳跃展开是格点量子色动力学中夸克传播子的一个重要近似，但它对于轻夸克发散，这限制了强耦合和有限密度模拟的范围。我们提出了一种人工科学发现框架，使用克利福德基下的规范等变神经网络来提取可解释的近似。我们的方法产生了改进的低阶近似，在相同计算成本下优于跳跃展开。在跳跃展开失效的参数区域，我们的方法识别出收敛行为，从而提出了一种新的级数展开。所发现的近似可推广到未见过的规范场和体积，使其能够立即作为即插即用替代品实施，并为夸克传播子展开提供新的理论理解。
英文摘要：
The hopping expansion is an important approximation of the quark propagator in lattice quantum chromodynamics, but it diverges for light quarks, which limits the scope of strong-coupling and finite-density simulations. We present an artificial scientific discovery framework using gauge-equivariant neural networks in the Clifford basis to extract interpretable approximations. Our approach yields improved low-order approximations that outperform the hopping expansion at identical computational cost. In the parameter regime where the hopping expansion fails, our approach identifies convergent behavior, thus suggesting a novel series expansion. The discovered approximations generalize to unseen gauge fields and volumes, enabling immediate implementation as plug-in replacements and a new theoretical understanding of quark-propagator expansions.
