通过极化中子反射测量与机器学习阐明邻近磁性
Elucidating proximity magnetism through polarized neutron reflectometry and machine learning
AI总结：
本文提出基于变分自编码器的数据驱动框架，从极化中子反射测量数据中自动提取多层磁性材料参数，并成功应用于拓扑绝缘体异质结的邻近磁性分析。
摘要：
极化中子反射测量是一种强大的技术，能够以深度敏感性和纳米级分辨率探测多层磁性材料的结构。然而，使用传统拟合方法时，反射率曲线往往处于复杂的目标函数景观中，这对参数提取构成了重大挑战。在这项工作中，我们开发了一个数据驱动框架，以最少的人工干预从极化中子反射测量数据中恢复样品参数。我们训练了一个变分自编码器，将具有中等实验噪声的反射率曲线映射到一个可解释的低维空间，从中可以高分辨率地提取样品参数。我们将该方法应用于恢复拓扑绝缘体-铁磁绝缘体异质结Bi$_2$Se$_3$/EuS的散射长度密度剖面，该异质结表现出邻近磁性，结果与传统拟合结果吻合良好。我们进一步分析了拓扑绝缘体-反铁磁体异质结(Bi,Sb)$_2$Te$_3$/Cr$_2$O$_3$的更具挑战性的反射率曲线，并识别出该材料中可能存在的界面邻近磁性。我们预计，这里开发的框架可应用于解析广泛层状系统中隐藏的界面现象。
英文摘要：
Polarized neutron reflectometry is a powerful technique to interrogate the structures of multilayered magnetic materials with depth sensitivity and nanometer resolution. However, reflectometry profiles often inhabit a complicated objective function landscape using traditional fitting methods, posing a significant challenge to parameter retrieval. In this work, we develop a data-driven framework to recover the sample parameters from polarized neutron reflectometry data with minimal user intervention. We train a variational autoencoder to map reflectometry profiles with moderate experimental noise to an interpretable, low-dimensional space from which sample parameters can be extracted with high resolution. We apply our method to recover the scattering length density profiles of the topological insulator-ferromagnetic insulator heterostructure Bi$_2$Se$_3$/EuS exhibiting proximity magnetism, in good agreement with the results of conventional fitting. We further analyze a more challenging reflectometry profile of the topological insulator-antiferromagnet heterostructure (Bi,Sb)$_2$Te$_3$/Cr$_2$O$_3$ and identify possible interfacial proximity magnetism in this material. We anticipate the framework developed here can be applied to resolve hidden interfacial phenomena in a broad range of layered systems.
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