Cygnus A jointly calibrated and imaged via non-convex optimisation from VLA data
Arwa Dabbech, Audrey Repetti, Rick A. Perley, Oleg M. Smirnov, Yves, Wiaux

TL;DR
This paper introduces a novel non-convex optimization method for joint calibration and imaging in radio interferometry, improving resolution and fidelity of radio maps by promoting sparsity with a log-sum prior, demonstrated on Cygnus A data.
Contribution
It proposes a new sparsity-promoting prior and an iterative non-convex optimization approach for joint calibration and imaging in radio interferometry, enhancing image quality.
Findings
Enhanced data fidelity and resolution in radio maps.
Successful detection of background sources near Cygnus A.
Effective modeling of direction-dependent antenna gains.
Abstract
Radio interferometric (RI) data are noisy under-sampled spatial Fourier components of the unknown radio sky affected by direction-dependent antenna gains. Failure to model these antenna gains accurately results in a radio sky estimate with limited fidelity and resolution. The RI inverse problem has been recently addressed via a joint calibration and imaging approach which consists in solving a non-convex minimisation task, involving suitable priors for the DDEs, namely temporal and spatial smoothness, and sparsity for the unknown radio map via an -norm prior, in the context of realistic RI simulations. Building on these developments, we propose to promote sparsity of the radio map via a log-sum prior, enforcing sparsity more strongly than the -norm. The resulting minimisation task is addressed via a sequence of non-convex minimisation tasks composed of re-weighted…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
