谱线窄化的对数高斯-考克斯过程

IF 1.7 Q2 MATHEMATICS, APPLIED
T. Harkonen, Emma Hannula, M. Moores, E. Vartiainen, L. Roininen
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引用次数: 0

摘要

我们提出了一种统计模型,用于缩小光谱中的线形,这种线形很好地近似为洛伦兹函数或Voigt函数的线性组合。我们引入对数高斯Cox过程来表示峰值位置,从而为线窄化提供不确定性量化。该方法的贝叶斯公式允许鲁棒和显式包含先验信息作为模型参数的概率分布。信号及其参数的估计是使用顺序蒙特卡罗算法执行的,然后是确定峰值位置的优化步骤。我们的方法通过模拟研究得到验证,并应用于矿物学拉曼光谱。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
A log-Gaussian Cox process with sequential Monte Carlo for line narrowing in spectroscopy
We propose a statistical model for narrowing line shapes in spectroscopy that are well approximated as linear combinations of Lorentzian or Voigt functions. We introduce a log-Gaussian Cox process to represent the peak locations thereby providing uncertainty quantification for the line narrowing. Bayesian formulation of the method allows for robust and explicit inclusion of prior information as probability distributions for parameters of the model. Estimation of the signal and its parameters is performed using a sequential Monte Carlo algorithm followed by an optimization step to determine the peak locations. Our method is validated using a simulation study and applied to a mineralogical Raman spectrum.
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CiteScore
3.30
自引率
0.00%
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