Parallel faceted imaging in radio interferometry via proximal splitting (Faceted HyperSARA): I. Algorithm and simulations

Upcoming radio interferometers are aiming to image the sky at new levels of resolution and sensitivity, with wide-band image cubes reaching close to the petabyte scale for SKA. Modern proximal optimization algorithms have shown a potential to significantly outperform clean thanks to their ability to inject complex image models to regularize the inverse problem for image formation from visibility data. They were also shown to be parallelizable over large data volumes thanks to a splitting functionality enabling the decomposition of the data into blocks, for parallel processing of block-specific data-fidelity terms involved in the objective function. Focusing on intensity imaging, the splitting functionality is further exploited in this work to decompose the image cube into spatiospectral facets, and enables parallel processing of facet-specific regularization terms in the objective function, leading to the 'Faceted HyperSARA' algorithm. Reliable heuristics enabling an automatic setting of the regularization parameters involved in the objective are also introduced, based on estimates of the noise level, transferred from the visibility domain to the domains where the regularization is applied. Simulation results based on a matlab implementation and involving synthetic image cubes and data close to gigabyte size confirm that faceting can provide a major increase in parallelization capability when compared to the non-faceted approach (HyperSARA).

Automated summary

Upcoming radio interferometers aim to achieve high-resolution and high-sensitivity imaging of the sky, with the SKA reaching a petabyte scale. Modern proximal optimization algorithms outperform clean techniques by injecting complex image models for regularization and parallelizing large data volumes with splitting functionality. This study introduces the "Faceted HyperSARA" algorithm, which decomposes the image cube into spatiospectral facets and enables parallel processing of facet-specific regularization terms. The algorithm also incorporates reliable heuristics for automatic setting of regularization parameters. Simulation results confirm the increased parallelization capability of faceting compared to the non-faceted approach (HyperSARA).