How to make CLEAN variants faster using clustered components informed by the autocorrelation function

Context. The deconvolution, imaging, and calibration of data from radio interferometers is a challenging computational (inverse) problem. The upcoming generation of radio telescopes poses significant challenges to existing and well-proven data reduction pipelines, due to the large data sizes expected from these experiments and the high resolution and dynamic range. Aims. In this manuscript, we deal with the deconvolution problem. A variety of multiscalar variants to the classical <ns0:monospace>CLEAN</ns0:monospace> algorithm (the de facto standard) have been proposed in the past, often outperforming <ns0:monospace>CLEAN</ns0:monospace> at the cost of significantly increasing numerical resources. For this work our aim was to combine some of these ideas for a new algorithm, <ns0:monospace>Autocorr-CLEAN</ns0:monospace>, to accelerate the decon-volution, and to prepare the data reduction pipelines for the data sizes expected from the upcoming generation of instruments. Methods. To this end, we propose using a cluster of <ns0:monospace>CLEAN</ns0:monospace> components fitted to the autocorrelation function of the residual in a subminor loop, to derive continuously changing and potentially nonradially symmetric basis functions for <ns0:monospace>CLEAN</ns0:monospace>ing the residual. Results. <ns0:monospace>Autocorr-CLEAN</ns0:monospace> allows the superior reconstruction fidelity achieved by modern multiscalar approaches, and their superior convergence speed. It achieves this without utilizing any substeps of super-linear complexity in the minor loops, keeping the single minor loop and subminor loop iterations at an execution time comparable to that of <ns0:monospace>CLEAN</ns0:monospace>. Combining these advantages, <ns0:monospace>Autocorr-CLEAN</ns0:monospace> is found to be up to a magnitude faster than the classical <ns0:monospace>CLEAN</ns0:monospace> procedure. Conclusions. <ns0:monospace>Autocorr-CLEAN</ns0:monospace> fits well in the algorithmic framework common for radio interferometry, making it relatively straightforward to include in future data reduction pipelines. With its accelerated convergence speed, and smaller residual, <ns0:monospace>Autocorr-CLEAN</ns0:monospace> may be an important asset for data analysis in the future.