Statistical estimation of mean Lorentzian line width in spectra by Gaussian processes

Abstract

We propose a statistical approach for estimating the mean line width in spectra comprising Lorentzian, Gaussian, or Voigt line shapes. Our approach uses Gaussian processes in two stages to jointly model a spectrum and its Fourier transform. We generate statistical samples for the mean line width by drawing realizations for the Fourier transform and its derivative using Markov chain Monte Carlo methods. In addition to being fully automated, our method enables well-calibrated uncertainty quantification of the mean line width estimate through Bayesian inference. We validate our method using a simulation study and apply it to an experimental Raman spectrum of $\beta$-carotene.