论文标题
拓扑缺陷和与机器学习的限制：紧凑型电动力学中的单孔的情况
Topological defects and confinement with machine learning: the case of monopoles in compact electrodynamics
论文作者
论文摘要
我们研究了机器学习技术的优势，以识别量子场理论中拓扑对象的动态。我们将三个时空维度中的紧凑型u（1）理论视为表现出限制和质量差距现象的理论的最简单例子。我们训练一个具有生成的单极构型的神经网络，以区分限制阶段，从中可以确定解元过渡点并预测几个可观察到的物品。该模型使用监督的学习方法，并将单极构型视为三维图像（全息图）。我们表明该模型可以精确地确定过渡温度，这取决于算法中实现的标准。更重要的是，我们在对其他晶格尺寸的配置进行预测之前，用单个晶格大小的配置训练神经网络，从中可以从中获得对临界温度的可靠估计。
We investigate the advantages of machine learning techniques to recognize the dynamics of topological objects in quantum field theories. We consider the compact U(1) gauge theory in three spacetime dimensions as the simplest example of a theory that exhibits confinement and mass gap phenomena generated by monopoles. We train a neural network with a generated set of monopole configurations to distinguish between confinement and deconfinement phases, from which it is possible to determine the deconfinement transition point and to predict several observables. The model uses a supervised learning approach and treats the monopole configurations as three-dimensional images (holograms). We show that the model can determine the transition temperature with accuracy, which depends on the criteria implemented in the algorithm. More importantly, we train the neural network with configurations from a single lattice size before making predictions for configurations from other lattice sizes, from which a reliable estimation of the critical temperatures are obtained.
