MORESANE: MOdel REconstruction by Synthesis-ANalysis Estimators. A sparse deconvolution algorithm for radio interferometric imaging (1412.5387v1)

Abstract: (arXiv abridged abstract) The current years are seeing huge developments of radio telescopes and a tremendous increase of their capabilities. Such systems make mandatory the design of more sophisticated techniques not only for transporting, storing and processing this new generation of radio interferometric data, but also for restoring the astrophysical information contained in such data. In this paper we present a new radio deconvolution algorithm named MORESANE and its application to fully realistic simulated data of MeerKAT, one of the SKA precursors. This method has been designed for the difficult case of restoring diffuse astronomical sources which are faint in brightness, complex in morphology and possibly buried in the dirty beam's side lobes of bright radio sources in the field. MORESANE is a greedy algorithm which combines complementary types of sparse recovery methods in order to reconstruct the most appropriate sky model from observed radio visibilities. A synthesis approach is used for the reconstruction of images, in which the synthesis atoms representing the unknown sources are learned using analysis priors. We apply this new deconvolution method to fully realistic simulations of radio observations of a galaxy cluster and of an HII region in M31. We show that MORESANE is able to efficiently reconstruct images composed from a wide variety of sources from radio interferometric data. Comparisons with other available algorithms, which include multi-scale CLEAN and the recently proposed methods by Li et al. (2011) and Carrillo et al. (2012), indicate that MORESANE provides competitive results in terms of both total flux/surface brightness conservation and fidelity of the reconstructed model. MORESANE seems particularly well suited for the recovery of diffuse and extended sources, as well as bright and compact radio sources known to be hosted in galaxy clusters.