铜酸盐中dd激发的单离子模型的机器学习测试
Machine-learning test of the single-ion model for $dd$ excitations in cuprates
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中文总结 AI 辅助
研究利用局部单离子模型和卷积神经网络分析YBa₂Cu₃O₆和La₂CuO₄的共振非弹性X射线散射光谱中的dd激发，验证了机器学习工具对YBa₂Cu₃O₆的有效性，也揭示了单离子模型对La₂CuO₄的局限性。
中文摘要 AI 辅助
我们使用局部单离子模型研究了YBa₂Cu₃O₆和La₂CuO₄的共振非弹性X射线散射光谱中的dd激发。数据通过传统全局拟合和在相同理论框架内训练的卷积神经网络进行分析。对于YBa₂Cu₃O₆，两种方法获得的激发态能量一致，得出能量增加时的xy、3z² - r²、xz/yz序列。这一结果验证了机器学习工具用于分析以dd激发为主的RIXS光谱的有效性。相比之下，对于La₂CuO₄，两种方法未收敛到单一解，揭示了单离子模型在描述铜酸盐中dd激发的局限性，并指出了除纯局部图像之外的其他贡献在塑造高能激发中的作用。
英文摘要
We investigate $dd$ excitations in Resonant Inelastic X-ray Scattering spectra of YBa$_2$Cu$_3$O$_6$ and La$_2$CuO$_4$ using the local single-ion model. The data are analyzed by conventional global fitting and by a convolutional neural network trained within the same theoretical framework. For YBa$_2$Cu$_3$O$_6$, the excited state energies obtained with the two methods coincide, leading to the $xy$, $3z^2-r^2$, $xz/yz$ sequence for increasing energy. This result validates the use of machine learning tools for the analysis of RIXS spectra dominated by $dd$ excitations. By contrast, for La$_2$CuO$_4$, the two methods do not converge to a single solution, revealing the limitations of the single-ion model in describing $dd$ excitations in cuprates and pointing to the role of additional contributions beyond a purely local picture in shaping high-energy excitations.
