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.

翻译：本文提出了一种针对吸收光谱的贝叶斯谱反卷积方法。在传统分析中，吸收光谱数据的噪声机制从未得到恰当考虑。该分析通常采用最小二乘法，而从贝叶斯统计视角看，这种方法假设了高斯噪声。由于引入适当的噪声模型即可实现贝叶斯推断，我们将单光子吸收过程视为伯努利试验，并基于二项分布发展了一种贝叶斯谱反卷积方法。通过数值实验在多种条件下对人工数据进行了评估。结果表明，我们的方法不仅能够从吸收光谱数据中高精度估计参数，还能从吸收率较大、谱结构趋于平坦（以往无法分析）的光谱数据中推断出参数。