: Fast Bayesian radio interferometric imaging

Context. The aim of this paper is to reduce the computational complexity of the Bayesian imaging algorithm <ns0:monospace>resolve</ns0:monospace>, enabling the application of Bayesian imaging for larger datasets. Aims. By combining computational shortcuts of the <ns0:monospace>CLEAN</ns0:monospace> algorithm with the Bayesian imaging algorithm <ns0:monospace>resolve</ns0:monospace> we developed an accurate and fast imaging algorithm that we named <ns0:monospace>fast-resolve</ns0:monospace>. Methods. We validate the accuracy of the presented <ns0:monospace>fast-resolve</ns0:monospace> algorithm by comparing it with results from <ns0:monospace>resolve</ns0:monospace> on VLA Cygnus A data. Furthermore, we demonstrate the computational advantages of <ns0:monospace>fast-resolve</ns0:monospace> on a large MeerKAT ESO 137-006 dataset, which is computationally out of reach for <ns0:monospace>resolve</ns0:monospace>. 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 <ns0:monospace>fast-resolve</ns0:monospace> is about 144 times faster than <ns0:monospace>resolve</ns0:monospace>. For the MeerKAT dataset with multiple channels the computational increase in speed of <ns0:monospace>fast-resolve</ns0:monospace> is even greater.