LOFAR Sparse Image Reconstruction
H. Garsden, J. N. Girard, J. L. Starck, S. Corbel, C. Tasse, A., Woiselle, J. P. McKean, A.S. van Amesfoort, J. Anderson, I. M. Avruch, R., Beck, M. J. Bentum, P. Best, F. Breitling, J. Broderick, M. Br\"uggen, H. R., Butcher, B. Ciardi, F. de Gasperin, E. de Geus, M. de Vos

TL;DR
This paper introduces a sparse reconstruction method for LOFAR radio telescope imaging, demonstrating improved resolution and extended source imaging over traditional CLEAN methods, compatible with modern interferometric corrections.
Contribution
A novel sparse reconstruction approach based on compressed sensing theory integrated into LOFAR imaging, outperforming CLEAN in resolution and extended source recovery.
Findings
Sparse reconstruction matches CLEAN in point source flux recovery.
Significantly better for extended objects, reducing error by up to 10 times.
Achieves 2-3 times higher angular resolution than CLEAN images.
Abstract
Context. The LOw Frequency ARray (LOFAR) radio telescope is a giant digital phased array interferometer with multiple antennas distributed in Europe. It provides discrete sets of Fourier components of the sky brightness. Recovering the original brightness distribution with aperture synthesis forms an inverse problem that can be solved by various deconvolution and minimization methods Aims. Recent papers have established a clear link between the discrete nature of radio interferometry measurement and the "compressed sensing" (CS) theory, which supports sparse reconstruction methods to form an image from the measured visibilities. Empowered by proximal theory, CS offers a sound framework for efficient global minimization and sparse data representation using fast algorithms. Combined with instrumental direction-dependent effects (DDE) in the scope of a real instrument, we developed and…
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