Deep learning in exoplanet data analysis

With thousands of planets confirmed, tens of exoplanetary atmospheres observed, and several promising missions in preparation, the field of Exoplanetology is currently undergoing a complete revolution. Yet, it is still facing technical challenges, since planetary signals remains very faint no matter the detection technique, and instrument systematics numerous and loosely understood. Besides, the rapid growth of the observational data strongly suggests a shift towards more automated algorithms for the detection, de-trending and study of exoplanets. For these reasons, and in order to take advantage as much as possible of the current detectors and next generation of space-based transit-observers (JWST, ARIEL...), the whole analysis pipeline needs to be transformed, processing the raw data from the camera all the way up to the retrieval of the detrended transit light curves and transmission spectra. The current objective of this project is to build a deep learning framework allowing to deal with the amount and complexity of the input data coming from various exoplanets search projects.