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In this paper, we propose a Bayesian spectral deconvolution method forabsorption spectra. In conventional analysis, the noise mechanism of absorptionspectral data is never considered appropriately. In that analysis, theleast-squares method, which assumes Gaussian noise from the perspective ofBayesian statistics, is frequently used. Since Bayesian inference is possibleby introducing an appropriate noise model for the data, we consider theabsorption process of a single photon to be a Bernoulli trial and develop aBayesian spectral deconvolution method based on binomial distribution. We haveevaluated our method on artificial data under several conditions by numericalexperiments. The results show that our method not only allows us to estimateparameters with high accuracy from absorption spectral data, but also to inferthem even from absorption spectral data with large absorption rates where thespectral structure is flattened, which was previously impossible to analyze.

Bayesian Inference of Absorption Spectra Based on Binomial Distribution