Finding eclipsing binary star systems and measuring their physical properties using machine learning

The study of eclipsing binary star systems is one of the most mature and rewarding areas of stellar physics, offering the unique opportunity to determine the masses and radii of distant stars using only observational data and geometry.

This area of research is currently experiencing a renaissance, due to the remarkable quality and quantity of data coming from space-based searches for extrasolar planets such as the Kepler, K2 and TESS satellites.

These space missions produce such large datasets that it is vital to automate the processes of finding and analysing eclipsing binaries. The aim of this PhD project is to develop the computational tools needed to do so.

The tools can then be applied to data from the TESS satellite, and in future will be used for data from the PLATO satellite.

The methods used will include machine learning algorithms such as deep learning, image classification, and neural networks.

The fundamental aim of this project is to identify binary stars that are well suited to verifying and improving theoretical models of stars, which form the foundation of most areas of observational and theoretical astrophysics.