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. In this manuscript, we
deal with the deconvolution problem. A variety of multiscalar variants to the
classical CLEAN algorithm (the de-facto standard) have been proposed in the
past, often outperforming CLEAN at the cost of significantly increasing
numerical resources. In this work, we aim to combine some of these ideas for a
new algorithm, Autocorr-CLEAN, to accelerate the deconvolution and prepare the
data reduction pipelines for the data sizes expected by the upcoming generation
of instruments. To this end, we propose to use a cluster of CLEAN components
fitted to the autocorrelation function of the residual in a subminor loop, to
derive continuously changing, and potentially non-radially symmetric, basis
functions for CLEANing the residual. Autocorr-CLEAN allows for the superior
reconstruction fidelity achieved by modern multiscalar approaches, and their
superior convergence speed. It achieves this without utilizing any substep of
super-linear complexity in the minor loops, keeping the single minor loop and
subminor loop iterations at an execution time comparable to CLEAN. Combining
these advantages, Autocorr-CLEAN is found to be up to a magnitude faster than
the classical CLEAN procedure. Autocorr-CLEAN 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, Autocorr-CLEAN may be an important asset for the
data analysis in the future.

Cet article explore les excursions dans le temps et leurs implications.

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