Kuldeep Verma
Tata Institute of Fundamental Research, Mumbai, India

Asteroseismology of sun-like main-sequence stars: inferences using acoustic glitch and machine learning approach

The CoRoT and Kepler space missions have observed stellar oscillations in hundreds of sun-like stars. The measurements of the oscillation frequencies with unprecedented precision have already enhanced significantly our understanding of stellar interior and raised many questions. The future space observatories, e.g., TESS and PLATO, will take observations of possibly tens of thousands of stars, and can be used to thoroughly test various hypotheses of stellar evolution models. In this presentation, I shall talk about acoustic glitches and how they can be used to infer certain stellar properties in a manner that is either independent or less dependent on stellar evolution models. I shall also talk about a fully automated machine learning approach to do efficient stellar model fitting, which will help in analyzing the flood of high quality data that is expected to come from TESS and PLATO missions.