Bayesian Spectral Deconvolution of X-Ray Absorption Near Edge Structure Discriminating High- and Low-Energy Domain
share
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 the energy domain and a prior distribution that reflects the physical properties. We compare the conventional and proposed models in 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 to the electronic transition analysis by first-principles simulation.
READ FULL TEXT