2024ApJ...960...94P

Asteroseismology is a powerful tool to probe stellar structure. Spaceborne instruments like CoRoT, Kepler, and TESS have observed the oscillations of numerous stars, among which δ Scutis are particularly interesting, owing to their fast rotation and complex pulsation mechanisms. In this work, we inferred model-dependent masses, metallicities, and ages of 60 δ Scuti stars from photometric, spectroscopic, and asteroseismic observations using least-squares minimization. These statistics have the potential to explain why only a tiny fraction of δ Scuti stars pulsate in a very clean manner. We find most of these stars with masses around 1.6M_⊙ 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 (Δν) in studies of δ Scutis. 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 regarding 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 temperature T_eff strongly affects the inference of all structure parameters, and the asteroseismic offset parameter ϵ plays an essential role in the inference of age.