Titre :

Parallel faceted imaging in radio interferometry via proximal splitting (Faceted HyperSARA): I. Algorithm and simulations

Auteur(s) :

Thouvenin, Pierre-Antoine [Auteur]
Centre de Recherche en Informatique, Signal et Automatique de Lille - UMR 9189 [CRIStAL]
Université de Lille
Centrale Lille
Abdulaziz, Abdullah [Auteur]
Heriot-Watt University [Edinburgh] [HWU]
Dabbech, Arwa [Auteur]
Heriot-Watt University [Edinburgh] [HWU]
Repetti, Audrey [Auteur]
Heriot-Watt University [Edinburgh] [HWU]
Wiaux, Yves [Auteur]
Heriot-Watt University [Edinburgh] [HWU]

Titre de la revue :

Monthly Notices of the Royal Astronomical Society

Éditeur :

Oxford University Press (OUP): Policy P - Oxford Open Option A

Date de publication :

2022-11-11

ISSN :

0035-8711

Mot(s)-clé(s) en anglais :

Image processing
Radio interferometry imaging

Discipline(s) HAL :

Sciences de l'ingénieur [physics]/Traitement du signal et de l'image [eess.SP]
Planète et Univers [physics]/Astrophysique [astro-ph]/Instrumentation et méthodes pour l'astrophysique [astro-ph.IM]

Résumé en anglais : [en]

Upcoming radio interferometers are aiming to image the sky at new levels of resolution and sensitivity, with wide-band image cubes reaching close to the Petabyte scale for SKA. Modern proximal optimization algorithms have ...
Lire la suite >Upcoming radio interferometers are aiming to image the sky at new levels of resolution and sensitivity, with wide-band image cubes reaching close to the Petabyte scale for SKA. Modern proximal optimization algorithms have shown a potential to significantly outperform CLEAN thanks to their ability to inject complex image models to regularize the inverse problem for image formation from visibility data. They were also shown to be parallelizable over large data volumes thanks to a splitting functionality enabling the decomposition of the data into blocks, for parallel processing of block-specific data-fidelity terms involved in the objective function. Focusing on intensity imaging, the splitting functionality is further exploited in this work to decompose the image cube into spatio-spectral facets, and enable parallel processing of facet-specific regularization terms in the objective function, leading to the “Faceted HyperSARA” algorithm. Reliable heuristics enabling an automatic setting of the regularization parameters involved in the objective are also introduced, based on estimates of the noise level, transferred from the visibility domain to the domains where the regularization is applied. Simulation results based on a MATLAB implementation and involving synthetic image cubes and data close to Gigabyte size confirm that faceting can provide a major increase in parallelization capability when compared to the non-faceted approach (HyperSARA).Lire moins >

Langue :

Anglais

Comité de lecture :

Oui

Audience :

Internationale

Vulgarisation :

Non

Collections :

• Centre de Recherche en Informatique, Signal et Automatique de Lille (CRIStAL) - UMR 9189

Source :

Harvested from HAL