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Online radio interferometric imaging: Assimilating and discarding visibilities on arrival

Online radio interferometric imaging: Assimilating and discarding visibilities on arrival
Online radio interferometric imaging: Assimilating and discarding visibilities on arrival
The emerging generation of radio interferometric (RI) telescopes, such as the SquareKilometre Array (SKA), will acquire massive volumes of data and transition radio astronomy to a big-data era. The ill-posed inverse problem of imaging the raw visibilities acquired by RI telescopes will become significantly more computationally challenging, particularly in terms of data storage and computational cost. Current RI imaging methods, such as CLEAN, its variants, and compressive sensing approaches (i.e. sparse regularization), have yielded excellent reconstruction fidelity. However, scaling these methods to big-data remains difficult if not impossible in some cases. All state-of-the-art methods in RI imaging lack the ability to process data streams as they are acquired during the data observation stage. Such approaches are referred to as online processing methods. We present an online sparse regularization methodology for RI imaging. Image reconstruction is performed simultaneously with data acquisition, where observed visibilities are assimilated into the reconstructed image as they arrive and then discarded. Since visibilities are processed online, good reconstructions are recoveredmuch faster than standard (offline) methods that cannot start until the data acquisition stage completes. Moreover, the online method provides additional computational savings and, most importantly, dramatically reduces data storage requirements. Theoretically, the reconstructed images are of the same fidelity as those recovered by the equivalent offline approach and, in practice, very similar reconstruction fidelity is achieved. We anticipate that online imaging techniques, as proposed here, will be critical in scaling RI imaging to the emerging big-data era of radio astronomy.
Methods: Data analysis, methods: Numerical, techniques: Image processing, techniques: Interferometric
0035-8711
4559-4572
Cai, Xiaohao
de483445-45e9-4b21-a4e8-b0427fc72cee
Pratley, Luke
ef3709da-0dac-4ce5-bf00-ac3dc2384f9e
McEwen, Jason D.
64c6269a-fe40-41d7-8b0c-d3c9ad920175
Cai, Xiaohao
de483445-45e9-4b21-a4e8-b0427fc72cee
Pratley, Luke
ef3709da-0dac-4ce5-bf00-ac3dc2384f9e
McEwen, Jason D.
64c6269a-fe40-41d7-8b0c-d3c9ad920175

Cai, Xiaohao, Pratley, Luke and McEwen, Jason D. (2019) Online radio interferometric imaging: Assimilating and discarding visibilities on arrival. Monthly Notices of the Royal Astronomical Society, 485 (4), 4559-4572. (doi:10.1093/mnras/stz704).

Record type: Article

Abstract

The emerging generation of radio interferometric (RI) telescopes, such as the SquareKilometre Array (SKA), will acquire massive volumes of data and transition radio astronomy to a big-data era. The ill-posed inverse problem of imaging the raw visibilities acquired by RI telescopes will become significantly more computationally challenging, particularly in terms of data storage and computational cost. Current RI imaging methods, such as CLEAN, its variants, and compressive sensing approaches (i.e. sparse regularization), have yielded excellent reconstruction fidelity. However, scaling these methods to big-data remains difficult if not impossible in some cases. All state-of-the-art methods in RI imaging lack the ability to process data streams as they are acquired during the data observation stage. Such approaches are referred to as online processing methods. We present an online sparse regularization methodology for RI imaging. Image reconstruction is performed simultaneously with data acquisition, where observed visibilities are assimilated into the reconstructed image as they arrive and then discarded. Since visibilities are processed online, good reconstructions are recoveredmuch faster than standard (offline) methods that cannot start until the data acquisition stage completes. Moreover, the online method provides additional computational savings and, most importantly, dramatically reduces data storage requirements. Theoretically, the reconstructed images are of the same fidelity as those recovered by the equivalent offline approach and, in practice, very similar reconstruction fidelity is achieved. We anticipate that online imaging techniques, as proposed here, will be critical in scaling RI imaging to the emerging big-data era of radio astronomy.

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More information

Accepted/In Press date: 6 March 2019
e-pub ahead of print date: 11 March 2019
Published date: June 2019
Keywords: Methods: Data analysis, methods: Numerical, techniques: Image processing, techniques: Interferometric

Identifiers

Local EPrints ID: 438780
URI: http://eprints.soton.ac.uk/id/eprint/438780
ISSN: 0035-8711
PURE UUID: 476ecf03-3e32-46be-b7fb-eb611bf877c7

Catalogue record

Date deposited: 24 Mar 2020 17:30
Last modified: 06 Oct 2020 17:15

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Contributors

Author: Xiaohao Cai
Author: Luke Pratley
Author: Jason D. McEwen

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