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In a companion paper, a faceted wideband imaging technique for radiointerferometry, dubbed Faceted HyperSARA, has been introduced and validated onsynthetic data. Building on the recent HyperSARA approach, Faceted HyperSARAleverages the splitting functionality inherent to the underlying primal-dualforward-backward algorithm to decompose the image reconstruction over multiplespatio-spectral facets. The approach allows complex regularization to beinjected into the imaging process while providing additional parallelizationflexibility compared to HyperSARA. The present paper introduces new algorithmfunctionalities to address real datasets, implemented as part of a fullyfledged MATLAB imaging library made available on Github. A large scaleproof-of-concept is proposed to validate Faceted HyperSARA in a new data andparameter scale regime, compared to the state-of-the-art. The reconstruction ofa 15 GB wideband image of Cyg A from 7.4 GB of VLA data is considered,utilizing 1440 CPU cores on a HPC system for about 9 hours. The conductedexperiments illustrate the reconstruction performance of the proposed approachon real data, exploiting new functionalities to leverage knowndirection-dependent effects (DDEs), for an accurate model of the measurementoperator, and an effective noise level accounting for imperfect calibration.They also demonstrate that, when combined with a further dimensionalityreduction functionality, Faceted HyperSARA enables the recovery of a 3.6 GBimage of Cyg A from the same data using only 91 CPU cores for 39 hours. In thissetting, the proposed approach is shown to provide a superior reconstructionquality compared to the state-of-the-art wideband CLEAN-based algorithm of theWSClean software.

Parallel faceted imaging in radio interferometry via proximal splitting (Faceted HyperSARA): II. Code and real data proof of concept