Cygnus A super-resolved via convex optimisation from VLA data

TL;DR

This paper introduces a convex optimisation-based super-resolution imaging method for Cygnus A using VLA data, outperforming traditional techniques and revealing new astrophysical features.

Contribution

It develops an adaptive primal-dual algorithm leveraging sparsity priors for super-resolution imaging in radio interferometry, demonstrating improved results over CLEAN methods.

Findings

Super-resolved images with high fidelity beyond instrumental resolution.

Validation of high-resolution features against higher frequency maps.

Discovery of a radio transient in Cygnus A.

Abstract

We leverage the Sparsity Averaging Reweighted Analysis (SARA) approach for interferometric imaging, that is based on convex optimisation, for the super-resolution of Cyg A from observations at the frequencies 8.422GHz and 6.678GHz with the Karl G. Jansky Very Large Array (VLA). The associated average sparsity and positivity priors enable image reconstruction beyond instrumental resolution. An adaptive Preconditioned Primal-Dual algorithmic structure is developed for imaging in the presence of unknown noise levels and calibration errors. We demonstrate the superior performance of the algorithm with respect to the conventional CLEAN-based methods, reflected in super-resolved images with high fidelity. The high resolution features of the recovered images are validated by referring to maps of Cyg A at higher frequencies, more precisely 17.324GHz and 14.252GHz. We also confirm the recent discovery of a radio transient in Cyg A, revealed in the recovered images of the investigated data sets. Our matlab code is available online on GitHub.