共振超声光谱和机器学习揭示URu $_2$ Si $_2$单组分序参量
One-Component Order Parameter in URu$_2$Si$_2$ Uncovered by Resonant Ultrasound Spectroscopy and Machine Learning
摘要：
在T $_{HO}$ = 17.5 K以下的URu $_2$ Si $_2$中出现的不寻常的相关态被称为“隐藏序”，因为即使是序参量的基本特征，例如它的维数（无论它有一个分量还是两个分量），也是“隐藏的”。我们使用共振超声光谱测量跨T $_{HO}$的弹性异常。我们没有观察到异常的剪切弹性模量，提供了强有力的热力学证据的单组分序参量。我们开发了一个机器学习框架，可以直接从原始数据中得出这一结论，即使是在对于传统共振超声来说太小的晶体中。我们的结果排除了一个广泛的类的理论的基础上的两个组件的序参量的隐藏顺序，并限制了非常规超导性出现在较低的温度波动的性质。我们的机器学习框架是一个强大的新工具，用于分类相关电子系统中无处不在的竞争秩序。
英文摘要：
The unusual correlated state that emerges in URu$_2$Si$_2$ below T$_{HO}$ = 17.5 K is known as "hidden order" because even basic characteristics of the order parameter, such as its dimensionality (whether it has one component or two), are "hidden". We use resonant ultrasound spectroscopy to measure the symmetry-resolved elastic anomalies across T$_{HO}$. We observe no anomalies in the shear elastic moduli, providing strong thermodynamic evidence for a one-component order parameter. We develop a machine learning framework that reaches this conclusion directly from the raw data, even in a crystal that is too small for traditional resonant ultrasound. Our result rules out a broad class of theories of hidden order based on two-component order parameters, and constrains the nature of the fluctuations from which unconventional superconductivity emerges at lower temperature. Our machine learning framework is a powerful new tool for classifying the ubiquitous competing orders in correlated electron systems.
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