2009A&A...506...15B

Asteroseismology has entered a new era with the availability of continuous observations from space-borne missions such as MOST, CoRoT and Kepler. However, the low amplitude and the complexity of the observed spectrum make the exploitation of these data sets difficult. The use of robust methods to estimate the parameters of stellar oscillation eigenmodes is necessary to fully exploit these new data sets. These parameters include in particular the frequency, the width and the energy of the eigenmodes, all being required for a seismic interpretation of the stellar internal structure or excitation of the eigenmodes. A Bayesian approach, coupled with a Markov chain Monte Carlo (MCMC) algorithm, is presented. Such a method allows the use of a priori knowledge to improve the parameter estimation. It also provides complete information on the probability distribution of the fitted parameters. The method is tested on simulated time series and then applied to CoRoT observations of HD49933. The simulated time series allow the validation of the method for conditions similar to those of the observations in terms of spectral complexity and signal-to-noise ratio. However, a very important problem in the analysis of the HD49933 mode spectrum is the l degree identification of the modes. The degree identification has little impact on the large frequency separation, rotational splitting, energy and width estimation, whereas individual frequencies and the star inclination angle evaluation are strongly affected. From a statistical point of view, we provide a quantitative ranking of the four models considered. The most probable model includes only modes of degree 0 and 1. Two other models include modes with degree up to 2 and have a non negligible level of significance. The last model includes modes of degree 0 and 1 but has an alternate degree identification and can be definitively rejected. In conclusion, the significance of the resulting probabilities is not sufficient to draw a definite conclusion.