AI 中文总结
该研究针对XMCD传统分析不足，将其表述为反问题，基于神经网络从完整谱线形状重建自旋和轨道角动量，利用多体多重态计算数据训练并验证，证明可准确重建，获取超越传统分析的信息。
AI 中文摘要
X射线磁圆二色性（XMCD）是特定元素自旋和轨道角动量的有力探测手段。然而，基于求和规则的传统分析依赖积分光谱强度，当多个参数影响谱线形状时可能不足。本文将XMCD分析表述为一个反问题，并开发了一种基于神经网络的方法，直接从完整谱线形状重建自旋和轨道角动量。利用Fe、Co和Ni的L2,3边缘X射线吸收光谱（XAS）和XMCD光谱的多体多重态计算作为物理上明确的训练数据集，系统地改变包括晶体场分裂、自旋轨道耦合和交换场等关键参数。神经网络经过训练，将谱线形状映射到自旋和轨道角动量的期望值⟨Sz⟩和⟨Lz⟩，并使用严格的测试数据进行验证。结果证明了准确且无偏的重建，为从XAS和XMCD光谱进行数据驱动的反演重建建立了概念验证。这些发现表明，利用完整的XAS和XMCD谱线形状可获取超越传统求和规则分析的信息，同时与既定理论框架保持一致。
英文摘要
X-ray magnetic circular dichroism (XMCD) is a powerful probe of element-specific spin and orbital angular momentum. Conventional analyses based on sum rules, however, rely on integrated spectral intensities and can become insufficient when multiple parameters influence the spectral line shape. Here, we formulate XMCD analysis as an inverse problem and develop a neural-network (NN) based approach to reconstruct spin and orbital angular momentum directly from full spectral line shapes. Using many-body multiplet calculations of Fe, Co, and Ni $L_{2,3}$-edge X-ray absorption spectra (XAS) and XMCD spectra as a physically well-defined training dataset, we systematically vary key parameters including crystal-field splitting, spin--orbit coupling, and exchange field. The NN is trained to map spectral line shapes onto the expectation values of spin and orbital angular momentm $\langle S_z \rangle$ and $\langle L_z \rangle$, and validated using strictly test-only data. The results demonstrate accurate and unbiased reconstruction, establishing a proof of concept for data-driven inverse reconstruction from XAS and XMCD spectra. These findings show that exploiting the full XAS and XMCD line shapes provide access to information beyond conventional sum-rule analyses while remaining consistent with established theoretical frameworks.
Comments18 pages, 5 figures, 2 tables
