The population of hot Neptunes measured with TESS

This project will develop a planet validation pipeline which utilises machine learning approaches to calculate the posterior probability of transiting planetary candidates being true planets. Many false positive scenarios can mimic a transiting planet, such as bound or blended eclipsing binaries, and distinguishing between these and true planets is at the heart of transiting planet surveys. A machine learning based pipeline has the potential to provide rapid identification of true planets and to do so in a statistically robust way, allowing untangling of the relevant biases and the extraction of the underlying occurrence rate of planets as a function of orbital period and radius. Using the developed pipeline the student will study candidate planets from the NASA TESS satellite, focusing on the population of close in hot Neptunes and Jupiters. They will study the boundaries of the Neptunian desert, a region of parameter space typically devoid of planets, and refine our understanding of these strongly irradiated worlds. Such close in planets can have their outer envelopes evaporated, exposing the planetary interior. The overall average composition of planetary cores leaves imprints in the planet population, and the student will determine if these are discoverable using the TESS data.