A bayesian approach for the joint decomposition of a sequence of photoelectron spectra

This work deals with the decomposition of a temporal sequence of photoelectron spectra into a sum of peaks whose positions, amplitudes and widths are estimated. Since the peaks exhibit a (slow) evolution with time, the decomposition is performed jointly on the whole sequence to take this temporal information into account. To this end, we have developed a Bayesian model where a Markov random field favors a smooth evolution of peaks. The approach is unsupervised and a Gibbs sampler within a simulated annealing scheme enables to estimate the maximum a posteriori. We show the relevance of this approach compared with a method in which the spectra are decomposed separately and present an application on real photoelectron data.