Asteroseismic modelling strategies in the PLATO era

II. Automation of seismic inversions and quality assessment procedure

¹ Observatoire de Genève, Université de Genève, Chemin Pegasi 51, 1290 Versoix, Switzerland
e-mail: jerome.betrisey@unige.ch
² STAR Institute, University of Liège, 19C Allée du 6 Août, 4000 Liège, Belgium
³ LESIA, Observatoire de Paris, Université PSL, CNRS, Sorbonne Université, Université Paris-Cité, 5 place Jules Janssen, 92195 Meudon, France

Received: 28 July 2023
Accepted: 29 October 2023

Abstract

Context. In the framework of the PLATO mission, to be launched in late 2026, seismic inversion techniques will play a key role in determining the mission precision requirements in terms of stellar mass, radius, and age. It is therefore relevant to discuss the challenges of the automation of seismic inversions, which were originally developed for individual modelling.

Aims. We tested the performance of our newly developed quality assessment procedure of seismic inversions, which was designed for pipeline implementation.

Methods. We applied our assessment procedure to a testing set composed of 26 reference models. We divided our testing set into two categories: calibrator targets whose inversion behaviour is well known from the literature and targets for which we assessed the quality of the inversion manually. We then compared the results of our assessment procedure with our expectations as a human modeller for three types of inversions: the mean density inversion, the acoustic radius inversion, and the central entropy inversion.

Results. We find that our quality assessment procedure performs as well as a human modeller. The mean density inversion and the acoustic radius inversion are suited to large-scale applications, but not the central entropy inversion, at least in its current form.

Conclusions. Our assessment procedure shows promising results for a pipeline implementation. It is based on the by-products of the inversion and therefore requires few numerical resources to quickly assess the quality of an inversion result.