OPEN ACCESS
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.
RÉSUMÉ
Ce travail traite de la décomposition d’une séquence temporelle de spectres de photo-électrons en une somme de raies dont on estime les positions, amplitudes et largeurs. Comme les raies évoluent (lentement) dans le temps, la décomposition est effectuée conjointement sur toute la séquence afin de prendre en compte cette information temporelle. À cette fin, nous avons développé un modèle bayésien où un champ de Markov favorise une évolution douce des raies. L’approche est non supervisée et un échantillonneur de Gibbs couplé à un schéma de recuit simulé permet d’estimer le maximum a posteriori. Nous montrons la pertinence de cette approche par rapport à une méthode dans laquelle les spectres sont décomposés séparément et présentons une application sur données réelles de photo-électrons.
spectroscopic signal sequence decomposition, Bayesian inference, Markov chain Monte Carlo (MCMC) methods, simulated annealing, photoelectron spectroscopy
MOTS-CLÉS
décomposition d’une séquence de signaux spectroscopiques, inférence bayésienne, méthodes de Monte Carlo par chaînes de Markov (MCMC), recuit simulé, spectroscopie de photo-électrons
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