发表机构
Center for Geometry and Physics, Institute for Basic Science (IBS); Ulsan National Institute of Science and Technology（基础科学研究院几何与物理中心; 蔚山科学技术院）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本研究利用可解释AI（XAI）从Kasteleyn矩阵训练CNN，高准确率识别4d N=1规范理论膜平铺的几何不一致性，并通过梯度显著性分析定位需Higgsing的手性场，展示了XAI在理论物理缺陷诊断中的潜力。
AI 中文摘要
膜平铺是二维环面上的二部图，它们编码了由探测环带Calabi-Yau 3-fold的D3膜产生的4d N=1超对称规范理论的拉格朗日量。在这些二部图中，只有满足几何一致性条件的那些才对应于行为良好的量子场论。我们训练了一个卷积神经网络（CNN），直接从其Kasteleyn矩阵区分几何一致与不一致的膜平铺。我们研究了一族4d N=1理论，这些理论是通过向abelian orbifold C^3/Z_3 x Z_3的膜平铺的六边形面添加对角边而获得的，并发现CNN能够以高准确率识别几何不一致性。对于可以通过对单个双基本手性场进行Higgsing而变得一致的不一致膜平铺，我们展示了基于梯度的显著性分析可用于定位负责的手性场，其准确率远高于匹配的随机基线。这些结果表明，可解释人工智能（XAI）可用于识别导致超对称规范理论不一致性的局部缺陷。
英文摘要
Brane tilings are bipartite graphs on a 2-torus that encode the Lagrangians of 4d N=1 supersymmetric gauge theories arising on D3-branes probing toric Calabi-Yau 3-folds. Among these bipartite graphs, only those that satisfy geometric consistency conditions correspond to well-behaved quantum field theories. We train a convolutional neural network (CNN) to distinguish geometrically consistent from inconsistent brane tilings directly from their Kasteleyn matrices. We study a family of 4d N=1 theories obtained by adding diagonal edges to the hexagonal faces of the brane tiling for the abelian orbifold C^3/Z_3 x Z_3, and find that the CNN identifies geometric inconsistency with high accuracy. For inconsistent brane tilings that can be rendered consistent by Higgsing a single bifundamental chiral field, we show that gradient-based saliency analysis can be used to localize the responsible chiral fields with accuracy well above a matched random baseline. These results demonstrate that explainable AI (XAI) can be used to identify local defects responsible for inconsistencies in supersymmetric gauge theories.
Comments13 pages, 8 figures, 2 tables
