Abstract Details
| Name: Nipun Ghanghas Affiliation: TIFR Mumbai Conference ID: ASI2025_581 Title : Inferring Asteroseismic Parameters from Short Observations Using Deep Learning: Application to TESS and K2 Red Giants Authors and Co-Authors : Nipun Ghanghas 1, Siddharth Dhanpal 1, Shravan Hanasoge 1,2, Praneeth Netrapalli 3, Karthikeyan Shanmugam 3 Abstract Type : Poster Abstract Category : Stars, Interstellar Medium, and Astrochemistry in Milky Way Abstract : 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. Additionally, the mixed modes in red giants carry information from the core, which places strong constraints on stellar evolution. Using these mixed modes, we can estimate the period spacings of gravity modes, which is directly related to the core mass. 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 based method to infer asteroseismic parameters from short-duration observations. Specifically, we focus on two global seismic parameters, the large frequency separation (∆ν) and the frequency at maximum power (νmax), 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 (∆Π1), in addition to ∆ν and νmax. Our findings demonstrate that our machine learning algorithm can accurately infer ∆ν and νmax for approximately 50% of samples created by taking one-month Kepler and K2 observations. For TESS one sector data however, we recover reliable ∆ν for only about 10% of the stars. Additionally, we get reliable ∆Π1 inferences for about 90 young red-giants from K2. For these ∆Π1 inferences, we see a good match with the well known ∆ν−∆Π1 observed in Kepler red-giants. |