A Faceted Prior for Scalable Wideband Imaging: Application to Radio Astronomy

Pierre-Antoine Thouvenin, Abdullah Abdulaziz, Ming Jiang, Audrey Repetti, Yves Wiaux

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Wideband radio-interferometric (RI) imaging consists in estimating images of the sky across a whole frequency band from incomplete Fourier data. Powerful prior information is needed to regularize the inverse imaging problem. At the extreme resolution and dynamic range of interest to modern telescopes, image cubes will far exceed Terabyte sizes, with data volumes orders of magnitude larger, making image estimation a very challenging task. The computational cost and memory requirements of corresponding iterative image recovery algorithms are extreme and call for high parallelism. A data-splitting strategy was recently introduced to parallelize computations over data blocks within an advanced primal-dual convex optimization algorithm. Building on the same algorithm, we propose an image faceting approach that consists in splitting the image cube into 3D overlapping facets with their own prior, reducing the computational bottleneck from full image to facet size. Simulation results suggest our prior provides similar if not superior reconstruction quality to the corresponding state-of-the-art non-faceted approach, with facet parallelization offering acceleration and therefore increased potential of scalability to large data and image sizes.
Original languageEnglish
Title of host publication2019 IEEE International Workshop on Computational Advances in Multi-Sensor Adaptive Processing (CAMSAP)
Place of PublicationGuadeloupe, West Indies
Publication statusSubmitted - 19 Jul 2019

Fingerprint

Radio astronomy
Imaging techniques
Convex optimization
Telescopes
Frequency bands
Scalability
Data storage equipment
Recovery
Costs

Keywords

  • Wideband radio-interferometric imaging
  • facet-based prior
  • preconditioned primal-dual algorithm

Cite this

Thouvenin, P-A., Abdulaziz, A., Jiang, M., Repetti, A., & Wiaux, Y. (2019). A Faceted Prior for Scalable Wideband Imaging: Application to Radio Astronomy. Manuscript submitted for publication. In 2019 IEEE International Workshop on Computational Advances in Multi-Sensor Adaptive Processing (CAMSAP) Guadeloupe, West Indies.
Thouvenin, Pierre-Antoine ; Abdulaziz, Abdullah ; Jiang, Ming ; Repetti, Audrey ; Wiaux, Yves. / A Faceted Prior for Scalable Wideband Imaging: Application to Radio Astronomy. 2019 IEEE International Workshop on Computational Advances in Multi-Sensor Adaptive Processing (CAMSAP). Guadeloupe, West Indies, 2019.
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Thouvenin, P-A, Abdulaziz, A, Jiang, M, Repetti, A & Wiaux, Y 2019, A Faceted Prior for Scalable Wideband Imaging: Application to Radio Astronomy. in 2019 IEEE International Workshop on Computational Advances in Multi-Sensor Adaptive Processing (CAMSAP). Guadeloupe, West Indies.

A Faceted Prior for Scalable Wideband Imaging: Application to Radio Astronomy. / Thouvenin, Pierre-Antoine; Abdulaziz, Abdullah; Jiang, Ming; Repetti, Audrey; Wiaux, Yves.

2019 IEEE International Workshop on Computational Advances in Multi-Sensor Adaptive Processing (CAMSAP). Guadeloupe, West Indies, 2019.

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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AB - Wideband radio-interferometric (RI) imaging consists in estimating images of the sky across a whole frequency band from incomplete Fourier data. Powerful prior information is needed to regularize the inverse imaging problem. At the extreme resolution and dynamic range of interest to modern telescopes, image cubes will far exceed Terabyte sizes, with data volumes orders of magnitude larger, making image estimation a very challenging task. The computational cost and memory requirements of corresponding iterative image recovery algorithms are extreme and call for high parallelism. A data-splitting strategy was recently introduced to parallelize computations over data blocks within an advanced primal-dual convex optimization algorithm. Building on the same algorithm, we propose an image faceting approach that consists in splitting the image cube into 3D overlapping facets with their own prior, reducing the computational bottleneck from full image to facet size. Simulation results suggest our prior provides similar if not superior reconstruction quality to the corresponding state-of-the-art non-faceted approach, with facet parallelization offering acceleration and therefore increased potential of scalability to large data and image sizes.

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Thouvenin P-A, Abdulaziz A, Jiang M, Repetti A, Wiaux Y. A Faceted Prior for Scalable Wideband Imaging: Application to Radio Astronomy. In 2019 IEEE International Workshop on Computational Advances in Multi-Sensor Adaptive Processing (CAMSAP). Guadeloupe, West Indies. 2019