Abstract We develop an ensemble machine learning framework to estimate key asteroseismic parameters—namely the frequency of the highest peak ( ν ( A max )), the frequency of maximum oscillation power ( ν max ), and the large frequency separation (Δ ν )—directly from TESS light curves of δ Scuti stars ( δ Sct stars). For each light curve, we extract a set of features comprising statistical moments, Principal Component Analysis, autocorrelation function, spectral features from the fast Fourier transform, and multiscale features from the discrete wavelet transform. These features are used to train a stacked regressor model composed of Random Forest, Gradient Boosting, and Ridge Regressors. We train and evaluate the model using 583 δ Sct stars randomly selected from a total of 643 samples, repeatedly applying random 80/20 splits across 100 iterations. The model achieves high predictive accuracy with R 2 scores exceeding 0.77 for all asteroseismic quantities. We validate generalizability by applying the trained model to the remaining 60 δ Sct stars, not seen during training. The predicted values show strong agreement with traditional asteroseismic measurements, confirming the effectiveness of this framework for large-scale, automated asteroseismic analysis. Furthermore, the proposed framework allows for the estimation of asteroseismic indices across new 251 δ Sct stars.
Bolouki et al. (Fri,) studied this question.