MORESANE: MOdel REconstruction by Synthesis-ANalysis Estimators

Abstract

Context. Recent years have been seeing huge developments of radio telescopes and a tremendous increase in their capabilities (sensitivity, angular and spectral resolution, field of view, etc.). Such systems make designing more sophisticated techniques mandatory 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. Aims. 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 that are faint in brightness, complex in morphology, and possibly buried in the dirty beam's side lobes of bright radio sources in the field. Methods. MORESANE is a greedy algorithm that 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 reconstructing images, in which the synthesis atoms representing the unknown sources are learned using analysis priors. We applied this new deconvolution method to fully realistic simulations of the radio observations of a galaxy cluster and of an HII region in M 31. Results. We show that MORESANE is able to efficiently reconstruct images composed of a wide variety of sources (compact pointlike objects, extended tailed radio galaxies, low-surface brightness emission) from radio interferometric data. Comparisons with the state of the art algorithms indicate that MORESANE provides competitive results in terms of both the total flux/surface brightness conservation and fidelity of the reconstructed model. MORESANE seems particularly well suited to recovering diffuse and extended sources, as well as bright and compact radio sources known to be hosted in galaxy clusters.

Superresolution Full-polarimetric Imaging for Radio Interferometry with Sparse Modeling

“…Future work for the techniques proposed in this paper will include other sparse regularizations for multiresolution imaging, such as ℓ 1 +wavelet/curvelet transformation (e.g., Li et al 2011;Carrillo et al 2014;Dabbech et al 2015;Garsden et al 2015). In addition, the application of and experimentation with other forms of TV would be important.…”

“…A promising approach to overcoming this issue is to change the basis of the image to a more sparse one. Pioneering work in this area has made use of transforms to wavelet or curvelet bases, in which the image can be represented sparsely (e.g., Li et al 2011;Carrillo et al 2014;Dabbech et al 2015;Garsden et al 2015). We have taken another approach by adding total variation (TV) regularization (e.g., Wiaux et al 2010;McEwen & Wiaux 2011;Uemura et al 2015;Chael et al 2016), which produces an image that is sparse in its gradient domain.…”

Non-convex optimization for self-calibration of direction-dependent effects in radio interferometric imaging

“…This framework has shown to give good reconstruction results in several application fields such as astronomical remote sensing (Bobin et al 2008), optical interferometric imaging (Auria et al 2014;Birdi et al 2017), and RI imaging (Wiaux et al 2009a;Li et al 2011;Garsden et al 2015;Dabbech et al 2015). In this context, the regularization function r in eq.…”

“…by using an 1 regularization term. Note that this approach has subsequently been investigated in several works (Wiaux et al 2009b;Wenger et al 2010;Li et al 2011;McEwen & Wiaux 2011;Carrillo et al 2012;Dabbech et al 2015;Onose et al 2017). Furthermore, in the field of RI imaging, the resulting minimization problem involves large dimensional variables, especially for the big data problems encountered in the era of modern radio telescopes such as the Square Kilometer Array (SKA) 1 and LOw Frequency ARray (LOFAR) 2 .…”

The SKA and Galaxy Cluster Science with the Sunyaev-Zel’dovich Effect

“…the MORESANE algorithm and the multi-scale version of CLEAN (Dabbech et al 2014)), ii) polarisation studies for targeted observations, iii) an extended feasibility study taking into account the full SKA1 frequency range (including also SKA1-LOW), iv) a detailed analysis using the configuration of the full SKA array aimed at the study of the SZ effect in galaxy clusters.…”