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The peak bagging analysis, namely the fitting and identification of singleoscillation modes in stars' power spectra, coupled to the very high-qualitylight curves of red giant stars observed by Kepler, can play a crucial role forstudying stellar oscillations of different flavor with an unprecedented levelof detail. A thorough study of stellar oscillations would thus allow for deepertesting of stellar structure models and new insights in stellar evolutiontheory. However, peak bagging inferences are in general very challengingproblems due to the large number of observed oscillation modes, hence of freeparameters that can be involved in the fitting models. Effciency and robustnessin performing the analysis is what may be needed to proceed further. For thispurpose, we developed a new code implementing the Nested Sampling Monte Carlo(NSMC) algorithm, a powerful statistical method well suited for Bayesiananalyses of complex problems. In this talk we show the peak bagging of a sampleof high signal-to-noise red giant stars by exploiting recent Kepler datasetsand a new criterion for the detection of an oscillation mode based on thecomputation of the Bayesian evidence. Preliminary results for frequencies andlifetimes for single oscillation modes, together with acoustic glitches, aretherefore presented.

Peak Bagging of red giant stars observed by Kepler: first results with a new method based on Bayesian nested sampling