| Name: | Subrata Kumar Panda |
| Affiliation: | Tata Institute of Fundamental Research, Mumbai |
| Conference ID : | ASI2024_948 |
| Title : | Asteroseismology applied to constrain structure parameters of δ Scuti stars |
| Authors : | Subrata Kumar Panda, Siddharth Dhanpal, Simon J. Murphy, Shravan Hanasoge, Timothy R. Bedding |
| Authors Affiliation: | Subrata Kumar Panda, Tata Institute of Fundamental Research, Colaba, Mumbai 400005, Maharashtra, India
Siddharth Dhanpal, Tata Institute of Fundamental Research, Colaba, Mumbai 400005, Maharashtra, India
Simon J. Murphy, Centre for Astrophysics, University of Southern Queensland, Toowoomba, QLD 4350, Australia
Shravan Hanasoge, Tata Institute of Fundamental Research, Colaba, Mumbai 400005, Maharashtra, India
Timothy R. Bedding, Sydney Institute for Astronomy (SIfA), School of Physics, University of Sydney, NSW 2006, Australia |
| Mode of Presentation: | Oral |
| Abstract Category : | Stars, Interstellar Medium, and Astrochemistry in Milky Way |
| 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 δ 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 δ 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 δ Sct stars
pulsate in a very clean manner. We find most of these stars with masses around 1.6 M ⊙ 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
(∆ν) in studies of δ 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 ε plays an essential role in the inference of age. |
