Inferring Asteroseismic Parameters from Short Observations Using Deep Learning: Application to TESS and K2 Red Giants

Asteroseismology is the study of resonant oscillations of stars to infer their internal structure and dynamics. It is also a powerful tool for precisely determining stellar parameters such as mass, radius, surface gravity, and age. The ongoing TESS mission, with its nearly complete sky coverage, presents a unique opportunity to uniformly probe stellar populations across the Milky Way. TESS is estimated to have observed more than 300,000 oscillating red giants, most of which have one to two months of observations. Given the scale of this dataset, we need a fast, efficient, and robust way to analyse the data. In this work, our objective is to develop a machine learning (ML) based method to infer asteroseismic parameters from short-duration observations. Specifically, we focus on two global seismic parameters, the large frequency separation ($\Delta\nu$) and the frequency at maximum power ($\nu_{\mathrm{max}}$), from one-month-long TESS observations of red giants. Meanwhile, for K2 data, our focus extends to inferring the period spacings of dipolar gravity modes ($\Delta\Pi_{1}$), in addition to $\Delta\nu$ and $\nu_{\mathrm{max}}$. Our findings demonstrate that our machine learning algorithm can accurately infer $\Delta\nu$ and $\nu_{\mathrm{max}}$ for approximately 50% of samples created by taking one-month Kepler and K2 observations. For TESS one sector data however, we recover reliable $\Delta\nu$ for only about 23% of the stars. Additionally, we get reliable $\Delta\Pi_{1}$ inferences for about 200 young red-giants from K2. For these $\Delta\Pi_{1}$ inferences, we see a good match with the well known $\Delta\nu-\Delta\Pi_{1}$ degenerate sequence observed in Kepler red-giants.