  In this paper, we propose a Bayesian spectral deconvolution method for
absorption spectra. In conventional analysis, the noise mechanism of absorption
spectral data is never considered appropriately. In that analysis, the
least-squares method, which assumes Gaussian noise from the perspective of
Bayesian statistics, is frequently used. Since Bayesian inference is possible
by introducing an appropriate noise model for the data, we consider the
absorption process of a single photon to be a Bernoulli trial and develop a
Bayesian spectral deconvolution method based on binomial distribution. We have
evaluated our method on artificial data under several conditions by numerical
experiments. The results show that our method not only allows us to estimate
parameters with high accuracy from absorption spectral data, but also to infer
them even from absorption spectral data with large absorption rates where the
spectral structure is flattened, which was previously impossible to analyze.
