论文标题
无监督的机器学习对对称的淬火量规：概念验证演示
Unsupervised Machine Learning of Quenched Gauge Symmetries: A Proof-of-Concept Demonstration
论文作者
论文摘要
在冷凝物理物理学中，机器学习的目标之一是对物质阶段的分类。对系统的对称性的考虑可以大大帮助机器实现这一目标。我们证明了无监督的机器学习方案（主要组件分析方法）检测通过所谓的Mattis仪表转换引入的隐藏淬火量规对称性的能力。我们的工作表明，无监督的机器学习可以识别模型的隐藏特性，因此可能会为模型本身提供新的见解。
In condensed matter physics, one of the goals of machine learning is the classification of phases of matter. The consideration of a system's symmetries can significantly assist the machine in this goal. We demonstrate the ability of an unsupervised machine learning protocol, the Principal Component Analysis method, to detect hidden quenched gauge symmetries introduced via the so-called Mattis gauge transformation. Our work reveals that unsupervised machine learning can identify hidden properties of a model and may therefore provide new insights into the models themselves.
