A LOG-GAUSSIAN COX PROCESS WITH SEQUENTIAL MONTE CARLO FOR LINE NARROWING IN SPECTROSCOPY
Publication Name
Foundations of Data Science
Abstract
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.
Open Access Status
This publication may be available as open access
Volume
5
First Page
503
Last Page
519
Funding Number
327734
Funding Sponsor
Academy of Finland