A Stellar Model-fitting Pipeline for Asteroseismic Data from the Kepler Mission

TL;DR

This paper introduces an automated stellar model-fitting pipeline utilizing a parallel genetic algorithm to analyze Kepler asteroseismic data, demonstrated on solar data, aiming to enhance the reliability of stellar interior modeling.

Contribution

It presents a novel, objective pipeline for asteroseismic data analysis that improves model accuracy using genetic algorithms, applicable to Kepler data.

Findings

Successfully modeled the Sun's interior properties

Pipeline accurately reproduces known solar characteristics

Demonstrates potential for analyzing other Sun-like stars

Abstract

Over the past two decades, helioseismology has revolutionized our understanding of the interior structure and dynamics of the Sun. Asteroseismology will soon place this knowledge into a broader context by providing structural data for hundreds of Sun-like stars. Solar-like oscillations have already been detected from the ground in several stars, and NASA's Kepler mission is poised to unleash a flood of stellar pulsation data. Deriving reliable asteroseismic information from these observations demands a significant improvement in our analysis methods. In this paper we report the initial results of our efforts to develop an objective stellar model-fitting pipeline for asteroseismic data. The cornerstone of our automated approach is an optimization method using a parallel genetic algorithm. We describe the details of the pipeline and we present the initial application to Sun-as-a-star data, yielding an optimal model that accurately reproduces the known solar properties.