Joint deconvolution and blind source separation with non-coplanar interferometric data

With the fastly increasing development of multichannel imagers, blind source separation (BSS) algorithms are ubiquitous in astrophysics to unmix multispectral images. In this context, analyzing data from the forthcoming very large, continental-size, radio interferometers using BSS algorithms raises two challenges. Firstly, the data are incomplete and deteriorated by instrumental effects, which requires incorporating a deconvolution step to retrieve exploitable images. Secondly, the data are affected by non-coplanar effects that notably arise from the very large antenna baselines and which must be accounted for in the separation scheme. For this purpose, we introduce a joint non-coplanar deconvolution and BSS algorithm, called wGMCA. The algorithm is tested and characterized in many challenging configurations, showing remarkable robustness to initialization and inversion. It is compared to classical methods that process the deconvolution and separation separately; these tests demonstrate the advantage of performing the deconvolution and separation in a single pass.& COPY; 2023 Elsevier Inc. All rights reserved.

Automated summary

With the development of multichannel imagers, blind source separation (BSS) algorithms have become widely used in astrophysics to analyze multispectral images. However, applying BSS algorithms to data from large radio interferometers poses challenges due to incomplete and deteriorated data, as well as non-coplanar effects. To address these challenges, a joint non-coplanar deconvolution and BSS algorithm called wGMCA is introduced, which shows robustness and advantages compared to classical methods.