Characterising exoplanet atmospheres using deep neural networks

In the last two and a half decades, we have undergone what is best described as a second Copernican revolution. The discovery of extrasolar planets - i.e. planets orbiting other stars - has fundamentally transformed our understanding of planets, solar systems, their formation histories and our place in the grander scheme of the Milky Way. With the avalanche of recent discoveries (over 3500 confirmed and counting), we have begun to expand comparative planetology from our small scale statistics of 8 solar-system planets to a galactic understanding of planetary science. As the field matures from its initial discovery stage, we are facing entirely new challenges of large-samples, high-dimensional parameter spaces and big data. In order to uniformly characterise large numbers of exoplanets, we require significantly faster and more accurate classification algorithms than what current models provide. In this project, we are developing deep learning and machine learning solutions to help characterise the atmospheres of planets, ranging from our solar system objects to the most extreme hot-Jupiters and lava worlds.