Multiscale and multidirectional very long baseline interferometry imaging with CLEAN

Max-Planck-Institut für Radioastronomie, Auf dem Hügel 69, Bonn 53121, Germany
e-mail: hmueller@mpifr-bonn.mpg.de; alobanov@mpifr-bonn.mpg.de

Received: 2 August 2022
Accepted: 27 January 2023

Abstract

Context. Very long baseline interferometry (VLBI) is a radio-astronomical technique whereby the correlated signal from various baselines is combined into an image of the highest possible angular resolution. Due to the sparsity of the measurements, this imaging procedure constitutes an ill-posed inverse problem. For decades, the CLEAN algorithm has been the standard choice in VLBI studies, despite it bringing on some serious disadvantages and pathologies that are brought on by the requirements of modern frontline VLBI applications.

Aims. We developed a novel multiscale CLEAN deconvolution method (DoB-CLEAN) based on continuous wavelet transforms that address several pathologies in CLEAN imaging. We benchmarked this novel algorithm against CLEAN reconstructions on synthetic data and reanalyzed BL Lac observations of RadioAstron with DoB-CLEAN.

Methods. The DoB-CLEAN method approaches the image via multiscalar and multidirectional wavelet dictionaries. Two different dictionaries were used: 1) a difference of elliptical spherical Bessel functions dictionary fitted to the uv-coverage of the observation that is used to sparsely represent the features in the dirty image; 2) a difference of elliptical Gaussian wavelet dictionary that is well suited to represent relevant image features cleanly. The deconvolution was performed by switching between the dictionaries.

Results. DoB-CLEAN achieves a super-resolution compared to CLEAN and remedies the spurious regularization properties of CLEAN. In contrast to CLEAN, the representation via basis functions has a physical meaning. Hence, the computed deconvolved image still fits the observed visibilities, in contrast to CLEAN.

Conclusions. State-of-the-art multiscalar imaging approaches seem to outperform single-scalar standard approaches in VLBI and are well suited to maximize the extraction of information in ongoing frontline VLBI applications.