Bayesian Spectral Deconvolution of X-Ray Absorption Near Edge Structure Discriminating between High- and Low-Energy Domains

DOI DOI Web Site 被引用文献5件 参考文献13件 オープンアクセス
  • Shuhei Kashiwamura
    Graduate School of Science, The University of Tokyo, Bunkyo, Tokyo 113-0033, Japan
  • Shun Katakami
    Graduate School of Frontier Sciences, The University of Tokyo, Kashiwa, Chiba 277-8561, Japan
  • Ryo Yamagami
    Graduate School of Science and Technology, Kumamoto University, Kumamoto 860-8555, Japan
  • Kazunori Iwamitsu
    Technical Division, Kumamoto University, Kumamoto 860-8555, Japan
  • Hiroyuki Kumazoe
    Institute of Industrial Nanomaterials, Kumamoto University, Kumamoto 860-8555, Japan
  • Kenji Nagata
    Research and Services Division of Materials Data and Integrated System, National Institute for Materials Science, Tsukuba, Ibaraki 305-0047, Japan
  • Toshihiro Okajima
    Aichi Synchrotron Radiation Center, Seto, Aichi 489-0965, Japan
  • Ichiro Akai
    Institute of Industrial Nanomaterials, Kumamoto University, Kumamoto 860-8555, Japan
  • Masato Okada
    Graduate School of Science, The University of Tokyo, Bunkyo, Tokyo 113-0033, Japan

書誌事項

公開日
2022-07-15
DOI
  • 10.7566/jpsj.91.074009
  • 10.48550/arxiv.2203.09895
公開者
Physical Society of Japan

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説明

In this paper, we propose a Bayesian spectral deconvolution considering the properties of peaks in different energy domains. Bayesian spectral deconvolution regresses spectral data into the sum of multiple basis functions. Conventional methods use a model that treats all peaks equally. However, in X-ray absorption near edge structure (XANES) spectra, the properties of the peaks differ depending on the energy domain, and the specific energy domain of XANES is essential in condensed matter physics. We propose a model that discriminates between the low- and high-energy domains. We also propose a prior distribution that reflects the physical properties. We compare the conventional and proposed models in terms of computational efficiency, estimation accuracy, and model evidence. We demonstrate that our method effectively estimates the number of transition components in the important energy domain, on which the material scientists focus for mapping the electronic transition analysis by first-principles simulation.

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