Exoplanet detection in angular and spectral differential imaging with an accelerated proximal gradient algorithm

Differential imaging is a technique to post-process images captured by ground-based telescopes during an observation campaign, in order to make exoplanets in a distant planetary system directly visible and to remove the so-called quasi-static speckles that dramatically affect detection capabilities.In order to introduce geometric diversity between the exoplanets and the quasi-static speckles, the light is split into spectral channels during the data acquisition process, producing a 4-D data cube with images recorded at many wavelengths and at many times.In this work, we propose to follow an inverse problem approach to model the astronomical data as the contribution of a low-rank component containing the background of quasi-static speckles and a sparse component containing the exoplanets.We then formulate the resulting model as a convex non-smooth optimization model so that an accelerated proximal gradient descent can be used to solve the detection problem.