Radio Imaging With Information Field Theory

TL;DR

This paper introduces RESOLVE, a Bayesian radio interferometry imaging algorithm based on information field theory, with improved speed and stability for routine astronomical data analysis.

Contribution

The paper presents a new version of RESOLVE, enhancing its speed and stability through algorithmic improvements for practical radio astronomy imaging.

Findings

Significantly faster inference process.

Enhanced stability of the imaging algorithm.

Closer to routine application by astronomers.

Abstract

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.