Radio Imaging With Information Field Theory

Data from radio interferometers provide a substantial challenge for statisticians. It is incomplete, noise-dominated and originates from a non-trivial measurement process. The signal is not only corrupted by imperfect measurement devices but also from effects like fluctuations in the ionosphere that act as a distortion screen. In this paper we focus on the imaging part of data reduction in radio astronomy and present RESOLVE, a Bayesian imaging algorithm for radio interferometry in its new incarnation. It is formulated in the language of information field theory. Solely by algorithmic advances the inference could be sped up significantly and behaves noticeably more stable now. This is one more step towards a fully user-friendly version of RESOLVE which can be applied routinely by astronomers.