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

TL;DR

This paper introduces a new Bayesian nested sampling method for peak bagging analysis of red giant stars observed by Kepler, enabling detailed mode identification and stellar oscillation studies.

Contribution

The paper presents a novel Bayesian nested sampling approach for efficient and robust peak bagging analysis of stellar oscillations in red giants.

Findings

Successful peak bagging of high S/N red giant stars

Detection of oscillation mode frequencies and lifetimes

Identification of acoustic glitches in stellar oscillations

Abstract

The peak bagging analysis, namely the fitting and identification of single oscillation modes in stars' power spectra, coupled to the very high-quality light curves of red giant stars observed by Kepler, can play a crucial role for studying stellar oscillations of different flavor with an unprecedented level of detail. A thorough study of stellar oscillations would thus allow for deeper testing of stellar structure models and new insights in stellar evolution theory. However, peak bagging inferences are in general very challenging problems due to the large number of observed oscillation modes, hence of free parameters that can be involved in the fitting models. Effciency and robustness in performing the analysis is what may be needed to proceed further. For this purpose, we developed a new code implementing the Nested Sampling Monte Carlo (NSMC) algorithm, a powerful statistical method well suited for Bayesian analyses of complex problems. In this talk we show the peak bagging of a sample of high signal-to-noise red giant stars by exploiting recent Kepler datasets and a new criterion for the detection of an oscillation mode based on the computation of the Bayesian evidence. Preliminary results for frequencies and lifetimes for single oscillation modes, together with acoustic glitches, are therefore presented.