Spectral Analysis of X‐Ray Absorption Near Edge Structure in α‐Fe2O3 Based on Bayesian Spectroscopy,Physica Status Solidi (B) - Basic Solid State Physics

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Spectral Analysis of X‐Ray Absorption Near Edge Structure in α‐Fe2O3 Based on Bayesian Spectroscopy
Physica Status Solidi (B) - Basic Solid State Physics ( IF 1.5 ) Pub Date : 2020-06-24 , DOI: 10.1002/pssb.202000107
Kazunori Iwamitsu ₁ , Tatsuhiro Yokota ₂ , Koki Murata ₂ , Mao Kamezaki ₂ , Masaichiro Mizumaki ₃ , Tomoya Uruga ₃ , Ichiro Akai ₄

Affiliation

A Bayesian spectroscopy technique is proposed to decompose weak pre‐edge structures from an X‐ray absorption near edge structure (XANES) spectrum. This methodology can perform model selection based on an information criterion of Bayes free energy. To explain the entire XANES spectrum measured at Fe K‐edge of α‐Fe₂O₃ with a partial fluorescence yield method, a phenomenological model of an absorption edge structure and plural Gaussian peak structures is introduced. The absorption edge structure is constructed by a step profile by an arctangent function and an accompanying peak by a pseudo‐Voigt function. As the result of model selection for estimating the number K of Gaussian components, it is found that the models of

K = 13 - 15

have posterior probabilities of the model selection. In these models, in spite of the number of Gaussian components being different, the pre‐edge structure is definitely decomposed to three components, and it is found that posterior probability distributions of the parameters for these components hardly change in these models (

K = 13 - 15

). This result shows that an appropriate spectral decomposition of the pre‐edge structure is realized. In addition, the robustness of such spectral decomposition is confirmed based on the evaluation of estimating accuracy of all spectral parameters by their posterior probability distributions.

中文翻译：

基于贝叶斯光谱的α-Fe2O3中X射线吸收近边缘结构的光谱分析

提出了一种贝叶斯光谱技术，可以将弱的前边缘结构与X射线吸收近边缘结构（XANES）光谱分解。该方法可以基于贝叶斯自由能的信息准则执行模型选择。为了解释在的α-Fe的铁K边缘测得的整个频谱XANES ₂ ö ₃具有部分荧光收率方法，吸收边缘结构和复数高斯峰结构的唯象模型被引入。吸收边的结构是由反正切函数的阶梯分布和伪Voigt函数的伴随峰构成的。作为估计高斯分量数K的模型选择的结果，发现