标题： 机器学习临界自对偶伊辛-希格斯规范模型的算符内容 显示英文标题

标题： Machine Learning the Operator Content of the Critical Self-Dual Ising-Higgs Gauge Model

摘要： 我们研究了在自对偶线上最近成为争论主题的$(2+1)D$中的 Ising-Higgs 规范理论的临界性质。 首次使用机器学习技术，我们确定了相关场论的低能算符内容。 我们的方法使我们能够很大程度上反驳了涌现电流算符的存在，以及由此产生的该相变属于$XY^*$普适类的假设。 我们将这些结果与在$(2+1)D$Ashkin-Teller 横场 Ising 模型中得到的结果进行对比，我们在其中发现了预期的电流算符。 我们的数值技术扩展了最近提出的实空间互信息，使我们能够提取次级非线性算符。 这使得一种可控且计算可扩展的方法能够针对 CFT 谱并从蒙特卡洛数据中辨别超出$(1+1)D$的普适类。 显示英文摘要

摘要： We study the critical properties of the Ising-Higgs gauge theory in $(2+1)D$ along the self-dual line which have recently been a subject of debate. For the first time, using machine learning techniques, we determine the low energy operator content of the associated field theory. Our approach enables us to largely refute the existence of an emergent current operator and with it the standing conjecture that this transition is of the $XY^*$ universality class. We contrast these results with the ones obtained for the $(2+1)D$ Ashkin-Teller transverse field Ising model where we find the expected current operator. Our numerical technique extends the recently proposed Real-Space Mutual Information allowing us to extract sub-leading non-linear operators. This allows a controlled and computationally scalable approach to target CFT spectrum and discern universality classes beyond $(1+1)D$ from Monte Carlo data.