Supervised Machine Learning for Analysing Spectra of Exoplanetary Atmospheres

Pablo Marquez-Neila, Chloe Fisher, Raphael Sznitman, Kevin Heng (Submitted on 11 Jun 2018) The use of machine learning is becoming ubiquitous in astronomy, but remains rare in the study of the atmospheres of exoplanets. Given the spectrum of an exoplanetary atmosphere, a multi-parameter space is swept through in real time to find the best-fit model. Known as atmospheric retrieval, it is a technique that originates from the Earth and planetary sciences. Such methods are very time-consuming and by necessity there is a compromise between physical and chemical realism versus computational feasibility. Machine learning has previously been used to determine which molecules to include in the model, but the retrieval itself was still performed using standard methods.