Theoretical simulations are frequently used to assign electronic and geometric structure from spectral fingerprints. However, such assignments are prone to expectation bias. Bias can be reduced by using numerical measures of the similarity between calculated and experimental spectra. However, the commonly used point-wise comparisons cannot handle larger deviations in peak position. Here a weighted cross-correlation function is used to evaluate similarity scores for soft x-ray spectra of first-row transition metals. These spectra consist of hundreds of overlapping resonances, which makes spectral decomposition difficult. They are also challenging to model, leading to significant errors in both peak position and intensity. It is first shown how the choice of weight-function width can be related to the modeling errors. The method is then applied to evaluate the sensitivity of multiconfigurational wavefunction and charge-transfer multiplet simulations to model choices. The approach makes it possible to assess the reliability of assignments from spectral fingerprinting.

理论模拟通常用于从光谱指纹中分配电子和几何结构。但是，这样的分配容易产生期望偏差。可以通过使用数值计算的和实验光谱之间的相似性来减少偏差。但是，常用的逐点比较无法处理峰位置的较大偏差。在这里，加权互相关函数用于评估第一行过渡金属的软X射线光谱的相似性得分。这些光谱由数百个重叠的共振组成，这使光谱分解变得困难。它们也难以建模，导致峰位和强度均出现重大误差。首先显示了权重函数宽度的选择如何与建模误差相关。然后将该方法应用于评估多配置波函数和电荷转移多重模拟对模型选择的敏感性。该方法使得可以从频谱指纹分析评估分配的可靠性。