Automated extraction of oscillation parameters for Kepler observations of solar-type stars

TL;DR

This paper presents an automated pipeline for extracting key oscillation parameters from Kepler data of solar-like stars, enabling efficient analysis of large datasets with reliable uncertainty estimates.

Contribution

The authors develop and validate an automated method using autocorrelation and scaling relations to estimate oscillation parameters from Kepler observations, including uncertainty quantification.

Findings

Successfully determined oscillation parameters for about half of simulated stars.

Achieved 1% precision in stellar radii for 20% of the sample.

Demonstrated the pipeline's effectiveness for large-scale stellar analysis.

Abstract

The recent launch of the Kepler space telescope brings the opportunity to study oscillations systematically in large numbers of solar-like stars. In the framework of the asteroFLAG project, we have developed an automated pipeline to estimate global oscillation parameters, such as the frequency of maximum power (nu_max) and the large frequency spacing (Delta_nu), for a large number of time series. We present an effective method based on the autocorrelation function to find excess power and use a scaling relation to estimate granulation timescales as initial conditions for background modelling. We derive reliable uncertainties for nu_max and Delta_nu through extensive simulations. We have tested the pipeline on about 2000 simulated Kepler stars with magnitudes of V~7-12 and were able to correctly determine nu_max and Delta_nu for about half of the sample. For about 20%, the returned large frequency spacing is accurate enough to determine stellar radii to a 1% precision. We conclude that the methods presented here are a promising approach to process the large amount of data expected from Kepler.