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  4. S-R2D2: a spherical extension of the R2D2 deep neural network series paradigm for wide-field radio-interferometric imaging
 
research article

S-R2D2: a spherical extension of the R2D2 deep neural network series paradigm for wide-field radio-interferometric imaging

Tajja, A  
•
Aghabiglou, A
•
Tolley, E  
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July 3, 2025
Monthly Notices of the Royal Astronomical Society

Recently, the R2D2 paradigm, standing for ‘Residual-to-Residual DNN series for high-Dynamic-range imaging’, was introduced for image formation in Radio Interferometry (RI) as a learned version of the traditional algorithm CLEAN. The first incarnations of R2D2 are limited to planar imaging on small fields of view, failing to meet the spherical-imaging requirement of modern telescopes observing wide fields. To address this limitation, we propose the spherical-imaging extension S-R2D2. Firstly, as R2D2, S-R2D2 encapsulates its minor cycles in existing 2D-Euclidean deep neural network (DNN) architectures, but adapts its iterative scheme to incorporate the wide-field measurement model mapping a spherical image to visibility data. We implemented this model as the composition of an efficient Fourier-based interpolator mapping the spherical image onto the equatorial plane, with the standard RI operator mapping the equatorial-plane image to visibility data. Importantly, the interpolation step must inevitably be performed at a lower-than-optimal resolution on the plane, to meet the high-resolution requirement on the sphere of wide-field imaging while preserving scalability. Therefore, secondly, we design S-R2D2’s DNN training loss to jointly learn to correct the interpolation approximations and identify residual image structures on the sphere, ensuring consistency with the spherical ground truth using the adjoint plane-to-sphere interpolator. Finally, we demonstrate through simulations S-R2D2’s capability to perform fast and accurate reconstructions of spherical monochromatic intensity images, across high-resolution, high-dynamic-range settings.

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Type
research article
DOI
10.1093/mnras/staf1082
Author(s)
Tajja, A  

École Polytechnique Fédérale de Lausanne

Aghabiglou, A
Tolley, E  

École Polytechnique Fédérale de Lausanne

Kneib, J-P  

École Polytechnique Fédérale de Lausanne

Thiran, J-P  

École Polytechnique Fédérale de Lausanne

Wiaux, Y
Date Issued

2025-07-03

Publisher

Oxford University Press (OUP)

Published in
Monthly Notices of the Royal Astronomical Society
Editorial or Peer reviewed

REVIEWED

Written at

EPFL

EPFL units
LTS5  
LASTRO  
Available on Infoscience
July 5, 2025
Use this identifier to reference this record
https://infoscience.epfl.ch/handle/20.500.14299/251948
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