SpaceInn hare-and-hounds exercise: Estimation of stellar properties using space-based asteroseismic data

TL;DR

This study evaluates methods for deriving stellar properties from space-based asteroseismic data, demonstrating that forward modelling yields high accuracy and highlighting the importance of combining it with glitch analysis for reliable results.

Contribution

It presents a comprehensive comparison of forward modelling and glitch analysis methods for stellar characterization using artificial data, establishing their respective accuracies and limitations.

Findings

Forward modelling achieves <2% radius accuracy and ~4% mass accuracy.

Age estimates are accurate within 10% for 1 solar mass stars.

Glitch analysis can be affected by aliasing issues.

Abstract

Context: Detailed oscillation spectra comprising individual frequencies for numerous solar-type stars and red giants are or will become available. These data can lead to a precise characterisation of stars. Aims: Our goal is to test and compare different methods for obtaining stellar properties from oscillation frequencies and spectroscopic constraints, in order to evaluate their accuracy and the reliability of the error bars. Methods: In the context of the SpaceInn network, we carried out a hare-and-hounds exercise in which one group produced "observed" oscillation spectra for 10 artificial solar-type stars, and various groups characterised these stars using either forward modelling or acoustic glitch signatures. Results: Results based on the forward modelling approach were accurate to 1.5 % (radius), 3.9 % (mass), 23 % (age), 1.5 % (surface gravity), and 1.8 % (mean density). For the two 1 Msun stellar targets, the accuracy on the age is better than 10 % thereby satisfying PLATO 2.0 requirements. The average accuracies for the acoustic radii of the base of the convection zone, the He II ionisation, and the Gamma_1 peak were 17 %, 2.4 %, and 1.9 %, respectively. Glitch fitting analysis seemed to be affected by aliasing problems for some of the targets. Conclusions: Forward modelling is the most accurate approach, but needs to be complemented by model-independent results from, e.g., glitch analysis. Furthermore, global optimisation algorithms provide more robust error bars.