A Log-Gaussian Cox Process with Sequential Monte Carlo for Line Narrowing in Spectroscopy
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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 allowing for parallelization of model likelihood computation. The method is validated using simulated spectra and applied to an experimental Raman spectrum obtained from a protein droplet measurement.
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