Using Artificial Intelligence to unveil the secrets of stars from their vibrations

During all stages of evolution, stars oscillate as a superposition of normal modes of certain frequencies that are indicative of their internal structures and compositions. These vibrations are visible as surface brightness fluctuations, and have been observed by numerous telescopes. Despite the availability of an extensive sample of stars observed by the Kepler telescope, and the anticipated influx of additional data from missions such as TESS and the forthcoming PLATO, the full scientific exploitation of these datasets remains constrained by the conventional methods which require substantial computational time and resources for detailed analysis.



In this talk, I will discuss about asteroseismology and demonstrate how we leverage advanced deep learning techniques to facilitate rapid analysis of these datasets, especially those of post-main-sequence stars which have normal modes that enable us to investigate their interiors in significant detail, including rotation rates and magnetic fields.