Bayesian spectral deconvolution provides a data-driven framework for mathematical model selection and parameter estimation from spectral data. Although highly versatile, it becomes computationally expensive as the number of model parameters, data points, and candidate models increases, often rendering practical applications infeasible. We propose a GPU-accelerated approach in which a sequential Monte Carlo sampler (SMCS) is run in parallel on a GPU to perform Bayesian model selection of the number of spectral peaks and Bayesian estimation of peak-function parameters. Numerical experiments demonstrate that the GPU-parallelized SMCS achieves speedups exceeding 500x over CPU-parallelized replica exchange Monte Carlo (REMC). The method is validated on artificial data designed to emulate X-ray photoelectron spectroscopy (XPS) and X-ray diffraction (XRD) measurements, as well as on real experimental spectra. As measurement techniques such as microscopic spectroscopy and in-situ methods continue to drive rapid growth in the volume of spectral data, the proposed approach offers a practical computational foundation for advanced analysis of individual datasets.

翻译：贝叶斯谱反卷积为从谱数据中进行数学模型选择和参数估计提供了数据驱动框架。尽管该方法具有高度通用性，但随着模型参数、数据点和候选模型数量的增加，其计算成本急剧上升，往往导致实际应用难以实现。我们提出了一种GPU加速方法，通过在GPU上并行运行序贯蒙特卡洛采样器（SMCS），实现谱峰数量的贝叶斯模型选择以及峰函数参数的贝叶斯估计。数值实验表明，GPU并行化SMCS相比CPU并行化副本交换蒙特卡洛（REMC）实现了超过500倍的加速比。该方法在模拟X射线光电子能谱（XPS）和X射线衍射（XRD）测量的人工数据以及真实实验谱上均得到验证。随着显微光谱和原位方法等测量技术持续推动谱数据量的快速增长，所提出的方法为单个数据集的高级分析提供了实用的计算基础。