LOFAR sparse image reconstruction

H Garsden (1) , J.N Girard (1) , J.L. Starck (1) , Stéphane Corbel (1) , C Tasse (2) , A Woiselle (3) , J.P. Mckean (4) , Alexander S. van Amesfoort (5) , J Anderson (4) , I. M. Avruch (6) , R. Beck (6) , M.J. Bentum (5) , P. Best (7) , F Breitling (8) , J. Broderick (9, 10) , M. Brüggen (9) , Jean-Mathias Griessmeier (11, 12) , R Mcfadden (4) , D Mckay-Bukowski (13) , H Munk (4) , E Orru (4) , H Paas (14) , M Pandey-Pommier (15) , Michel Tagger (12)

1 AIM (UMR7158 / UMR_E_9005 / UM_112) - Astrophysique Interprétation Modélisation
2 LESIA - Laboratoire d'études spatiales et d'instrumentation en astrophysique
3 SAGEM Défense Sécurité [Massy]
4 ASTRON - Netherlands Institute for Radio Astronomy
5 TU Delft - Delft University of Technology
6 SRON - SRON Netherlands Institute for Space Research
7 University of Edinburgh
8 AIP - Leibniz-Institut für Astrophysik Potsdam
9 UHH - Universität Hamburg
10 University of Southampton
11 USN - Unité Scientifique de la Station de Nançay
12 LPC2E - Laboratoire de Physique et Chimie de l'Environnement et de l'Espace
13 University of Oulu
14 Center for Information Technology CIT
15 CRAL - Centre de Recherche Astrophysique de Lyon

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 validated a new method based on this framework. Methods. We implemented a sparse reconstruction method in the standard LOFAR imaging tool and compared the photometric and resolution performance of this new imager with that of CLEAN-based methods (CLEAN and MS-CLEAN) with simulated and real LOFAR data. Results. We show that i) sparse reconstruction performs as well as CLEAN in recovering the flux of point sources; ii) performs much better on extended objects (the root mean square error is reduced by a factor of up to 10); and iii) provides a solution with an effective angular resolution 2−3 times better than the CLEAN images. Conclusions. Sparse recovery gives a correct photometry on high dynamic and wide-field images and improved realistic structures of extended sources (of simulated and real LOFAR datasets). This sparse reconstruction method is compatible with modern interferometric imagers that handle DDE corrections (A-and W-projections) required for current and future instruments such as LOFAR and SKA.

Keywords: techniques: interferometric – methods: numerical – techniques: image processing

Domains: Sciences of the Universe [physics]

H Garsden, J.N Girard, J.L. Starck, Stéphane Corbel, C Tasse, et al.. LOFAR sparse image reconstruction. Astronomy and Astrophysics - A&A, 2015, 575, pp.A90. ⟨10.1051/0004-6361/201424504⟩. ⟨insu-01169967⟩