Deciphering stellar chorus: apollinaire, a Python 3 module for Bayesian peakbagging in helioseismology and asteroseismology

Since the asteroseismic revolution, the availability of efficient and reliable methods to extract stellar-oscillation mode parameters has been an important part of modern stellar physics. In the fields of helio- and asteroseismology, these methods are usually referred to as peakbagging . Here, we introduce the apollinaire module, a new Python 3 open-source Markov chain Monte Carlo (MCMC) framework dedicated to peakbagging . We extensively describe the theoretical framework necessary to understand MCMC peakbagging methods for disk-integrated helio- and asteroseismic observations. In particular, we present the models that are used to estimate the posterior probability function in a peakbagging framework. A description of the apollinaire module is then provided. We explain how the module enables stellar background, p-mode global pattern, and individual-mode parameter extraction. By taking into account instrumental specificities, stellar inclination angle, rotational splittings, and asymmetries, the module allows a large variety of p-mode models to be fitted that are suited for solar and stellar data analysis with different instruments. After presenting a validation of the module with a Monte Carlo fitting trial on synthetic data, it is benchmarked by comparing its outputs with results obtained with other peakbagging codes. We present our analysis of the power spectral density (PSD) of 89 one-year subseries of GOLF observations. We also selected six stars from the Kepler LEGACY sample in order to demonstrate the code abilities on asteroseismic data. The parameters we extract with apollinaire are in good agreement with those presented in the literature and demonstrate the precision and reliability of the module.