PhD thesis – Statistical modeling and physics-informed deep learning for the detection and characterization of high-contrast exoplanets from multidimensional data

Keywords: statistical modeling, physics-informed deep learning, data-driven approaches, inverse problems,
hybrid approaches, instrumental modeling, nuisance modeling, multivariate data, high-angular resolution &
high-contrast imaging, exoplanet detection & characterization.

Scientific Context: The direct observation of the close environment of stars can reveal the presence of exoplanets and circumstellar disks, providing crucial insights into the formation, evolution, and diversity of planetary systems [1]. Given the very small angular separation with respect to the host star and the huge contrast between the (very bright) star and the (very faint) exoplanets and disks, imaging the immediate vicinity of a star is extremely challenging. To overcome these difficulties, advanced observational techniques are used. They include (i) extreme adaptive optics, which compensates in real time for wavefront distortions caused by atmospheric turbulence; (ii) coronagraphy, which partially blocks the star light; and (iii) observing strategies leveraging the telescope’s pupil-tracking mode, which introduces diversity among the different signals to be unmixed [2]. Dedicated processing methods that combine the recorded spatio-temporo-spectral image series form the last corner-stone of direct imaging and they aim to efficiently suppress the nuisance component (i.e., speckles and noise) corrupting the signals of interest [3]. In this context, data science developments are decisive to improve the detection sensitivity of exoplanets and the accuracy of their physical characterization (i.e., spectrum and orbit estimation).