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

TL;DR

This paper introduces a Bayesian spectral deconvolution method tailored for XANES spectra, effectively distinguishing between low- and high-energy domains to improve estimation accuracy and computational efficiency in electronic transition analysis.

Contribution

The paper presents a novel Bayesian model that discriminates energy domains in XANES spectra, incorporating physical priors to enhance spectral component estimation.

Findings

Improved estimation accuracy over conventional methods.

Effective discrimination between energy domains.

Enhanced computational efficiency.

Abstract

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.