  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.
