  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.
