Oscillation frequencies for 35 \Kepler solar-type planet-hosting stars using Bayesian techniques and machine learning

TL;DR

This paper applies Bayesian and machine learning techniques to analyze Kepler data, measuring oscillation frequencies in 35 solar-type stars hosting exoplanets to improve stellar characterization.

Contribution

It introduces a novel combination of Bayesian peak bagging and machine learning for asteroseismic analysis of Kepler stars, providing detailed frequency measurements and covariance matrices.

Findings

Measured oscillation mode frequencies for all 35 stars.

Provided covariance matrices for frequency ratios.

Enhanced asteroseismic modeling accuracy.

Abstract

\Kepler has revolutionised our understanding of both exoplanets and their host stars. Asteroseismology is a valuable tool in the characterisation of stars and \Kepler is an excellent observing facility to perform asteroseismology. Here we select a sample of 35 \Kepler solar-type stars which host transiting exoplanets (or planet candidates) with detected solar-like oscillations. Using available \Kepler short cadence data up to Quarter 16 we create power spectra optimised for asteroseismology of solar-type stars. We identify modes of oscillation and estimate mode frequencies by ``peak bagging'' using a Bayesian MCMC framework. In addition, we expand the methodology of quality assurance using a Bayesian unsupervised machine learning approach. We report the measured frequencies of the modes of oscillation for all 35 stars and frequency ratios commonly used in detailed asteroseismic modelling. Due to the high correlations associated with frequency ratios we report the covariance matrix of all frequencies measured and frequency ratios calculated. These frequencies, frequency ratios, and covariance matrices can be used to obtain tight constraint on the fundamental parameters of these planet-hosting stars.