New generation radio-interferometers, such as the Karl G. Jansky Very Large Array (VLA) probe the radio sky at very high resolution and sensitivity. In this data acquisition regimes, calibration errors tend to dominate the thermal noise, resulting in the limited dynamic range of the recovered radio maps. In this context, we propose an adaptive strategy to estimate the noise and errors levels in the data. The procedure is incorporated in the recently proposed Preconditioned primal-dual algorithmic structure.
The code made available here ia a MATLAB implementation of the proposed algorithm.
A. Dabbech, A. Onose, A. Abdulaziz, R. A. Perley, O. M. Smirnov, Y. Wiaux - Cygnus A super-resolved via convex optimization from VLA data , MNRAS 2018, doi:0.1093/mnras/sty372