Asteroseismology applied to constrain structure parameters of {\delta} Scuti stars

TL;DR

This study uses asteroseismology, observational data, and machine learning to determine the structure parameters of 60 { extdelta} Scuti stars, revealing bimodal ages and emphasizing the importance of large-frequency separation.

Contribution

It introduces machine learning models for rapid inference of stellar parameters and highlights the significance of specific observables in constraining { extdelta} Scuti star structures.

Findings

Most stars have masses around 1.6 M_ and low metallicity.

A bimodal age distribution with young and old populations.

ML models improve parameter estimation and reveal key observable importance.

Abstract

Asteroseismology is a powerful tool to probe the structure of stars. Space-borne instruments like CoRoT, Kepler and TESS have observed the oscillations of numerous stars, among which {\delta} Scutis are particularly interesting owing to their fast rotation rates and complex pulsation mechanisms. In this work, we inferred model-dependent masses, metallicities and ages of 60 {\delta} Scuti stars from their photometric, spectroscopic and asteroseismic observations using least-squares minimization. These statistics have the potential to explain why only a tiny fraction of {\delta} Sct stars pulsate in a very clean manner. We find most of these stars with masses around 1.6 {M_\odot} and metallicities below Z = 0.010. We observed a bimodality in age for these stars, with more than half the sample younger than 30 Myr, while the remaining ones were inferred to be older, i.e., hundreds of Myrs. This work emphasizes the importance of the large-frequency separation ({\Delta\nu}) in studies of {\delta} Scuti stars. We also designed three machine learning (ML) models that hold the potential for inferring these parameters at lower computational cost and much more rapidly. These models further revealed that constraining dipole modes can help in significantly improving age estimation and that radial modes succinctly encode information pertaining to stellar luminosity and temperature. Using the ML models, we also gained qualitative insight into the importance of stellar observables in estimating mass, metallicity, and age. The effective surface temperature T eff strongly affects the inference of all structure parameters and the asteroseismic offset parameter {\epsilon} plays an essential role in the inference of age.