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 for 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 Bi2Se3/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)2Te3/Cr2O3and identify possible interfacial proximity magnetism in this material. We anticipate that the framework developed here can be applied to resolve hidden interfacial phenomena in a broad range of layered systems.

中文翻译：

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通过极化中子反射计和机器学习阐明邻近磁性

极化中子反射计是一种强大的技术，可以以深度灵敏度和纳米分辨率研究多层磁性材料的结构。然而，使用传统拟合方法，反射测量剖面通常存在复杂的目标函数景观，对参数检索提出了重大挑战。在这项工作中，我们开发了一个数据驱动的框架，以在最少的用户干预下从偏振中子反射数据中恢复样本参数。我们训练了一个变分自动编码器，将具有适度实验噪声的反射测量剖面映射到可解释的低维空间，从中可以高分辨率提取样本参数。我们应用我们的方法来恢复拓扑绝缘体 - 铁磁绝缘体异质结构 Bi 的散射长度密度分布2个硒3个/EuS 表现出的邻近磁性与传统拟合的结果非常一致。我们进一步分析了拓扑绝缘体 - 反铁磁体异质结构（Bi，Sb）的更具挑战性的反射测量曲线2个特3个/铬2个欧3个并确定该材料中可能存在的界面邻近磁性。我们预计此处开发的框架可用于解决广泛的分层系统中隐藏的界面现象。