Unsupervised joint Bayesian decomposition of a sequence of photoelectron spectra

This article presents a method for decomposing a temporal sequence of photoelectron spectra into a parameter set reflecting the positions, amplitudes, and widths of the peaks. Since the peaks exhibit a slow evolution with time, we propose to take into account this temporal information by jointly decomposing the whole sequence. To this end, we have developed a Bayesian model where a Gaussian Markov random field favors a smooth evolution of the peaks. The approach is made completely unsupervised and a Gibbs sampler with simulated annealing algorithm enables to estimate the maximum a posteriori. We show the accuracy of this approach compared to a method in which the spectra are decomposed separately and present an application on real photoelectron data.