SIMBAD references

Context. The aim of this paper is to reduce the computational complexity of the Bayesian imaging algorithm resolve, enabling the application of Bayesian imaging for larger datasets.
Aims. By combining computational shortcuts of the CLEAN algorithm with the Bayesian imaging algorithm resolve we developed an accurate and fast imaging algorithm that we named fast-resolve.
Methods. We validate the accuracy of the presented fast-resolve algorithm by comparing it with results from resolve on VLA Cygnus A data. Furthermore, we demonstrate the computational advantages of fast-resolve on a large MeerKAT ESO 137-006 dataset, which is computationally out of reach for resolve.
Results. The presented algorithm is significantly faster than previous Bayesian imaging algorithms, broadening the applicability of Bayesian interferometric imaging. Specifically, for the single channel VLA Cygnus A datasets fast-resolve is about 144 times faster than resolve. For the MeerKAT dataset with multiple channels the computational increase in speed of fast-resolve is even greater.