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.
@article{arxiv.1710.08810,
title = {Cygnus A super-resolved via convex optimisation from VLA data},
author = {Arwa Dabbech and Alexandru Onose and Abdullah Abdulaziz and Richard A. Perley and Oleg M. Smirnov and Yves Wiaux},
journal= {arXiv preprint arXiv:1710.08810},
year = {2018}
}
Comments
14 pages, 7 figures (3/7 animated figures), accepted for publication in MNRAS