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.
['Sylvain Mazet', 'Sylvain Faisan', 'Antoine Masson', 'M. A. Gaveau', 'Lionel Poisson', 'Jean-Michel Mestdagh']
Approche bayésienne pour la décomposition conjointe d'une séquence de spectres de photo-électrons