IViS: Interferometric Visibility-domain inversion Software. A GPU-accelerated Python framework for joint deconvolution with ASKAP
TL;DR
The current model reconstructs a nonparametric sky cube directly from calibrated visibilities by minimizing a regularized least-squares criterion, and illustrates the potential of regularized inverse-problem approaches as an alternative to traditional CLEAN-based imaging for next-generation radio surveys.
Abstract
Wide-field spectral-line imaging with modern radio interferometers remains challenging when the emission is extended, multiscale, and distributed over many overlapping pointings. In this regime, accurate reconstruction requires a joint treatment of the calibrated visibilities, control of image-domain regularity, and, when necessary, recovery of missing short spacings from single-dish data. We present of Galactic foreground emission and LMC emission with reduced residual side-lobe structure and realistic multiscale morphology. A comparison with using multiscale CLEAN shows that, for this specific target and a single observing block, (Interferometric Visibility-domain Inversion Software), a GPU-accelerated Python framework for visibility-domain joint deconvolution, and assess its performance for wide-field ̋I imaging with the Australian Square Kilometre Array Pathfinder (ASKAP). The current model, reconstructs a nonparametric sky cube directly from calibrated visibilities by minimizing a regularized least-squares criterion. The forward model relies on a nonuniform fast Fourier transform (NUFFT), supports joint deconvolution across all mosaic pointings, and allows positivity constraints through bounded optimization. Short-spacing information can be incorporated through a fusion term that models single-dish data from the same sky estimate. We validate the method using synthetic point-source, noise-only, and multiscale diffuse-emission simulations, and apply it to ASKAP full-survey observations toward the Large Magellanic Cloud (LMC). The simulations show that recovers point-source flux accurately in the absence of regularization and that the regularization strength sets the trade-off between noise suppression and effective resolution. Relative to linear mosaicking of independently deconvolved pointings, joint deconvolution can recover more large-scale power. When single-dish information is included, the reconstructed power spectra closely reproduce the input sky statistics over a broad range of spatial frequencies. Applied to ASKAP data, yields 10,h mosaics with an effective resolution of 22 ASKAPSoft exhibits fewer residual side-lobes and recovers substantially more power at low spatial frequencies. provides a flexible visibility-domain framework for joint deconvolution of large spectral-line mosaics, and illustrates the potential of regularized inverse-problem approaches as an alternative to traditional CLEAN-based imaging for next-generation radio surveys.