Two distributed algorithms for the deconvolution of large radio-interferometric multispectral images

Meillier, Céline; Bianchi, Pascal; Hachem, Walid

doi:10.1109/eusipco.2016.7760344

Cited by 7 publications

(12 citation statements)

References 5 publications

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“…The relevance of the intermediate variable p is that it allows to write problem (3) in an equivalent form which has a solvable dual problem. This is in contrast with our previous work [24]. This new formulation can be summarized by the following expression:…”

Section: Reformulation Of the Minimization Problemmentioning

confidence: 74%

Distribution strategies for very large 3D image deconvolution algorithms

Meillier

Ammanouil

Ferrari

et al. 2018

Signal Processing: Image Communication

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Section: Reformulation Of the Minimization Problemmentioning

confidence: 74%

Distribution strategies for very large 3D image deconvolution algorithms

Meillier

Ammanouil

Ferrari

et al. 2018

Signal Processing: Image Communication

Self Cite

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“…In a situation where the number of available compute agents is much less than the number of frequencies at which data are available, this may become problematic. Similar problems have been encountered in radio interferometric imaging where the simultaneous use of all available data is a daunting task for which multiple solutions have been proposed (Meillier et al 2016;Deguignet et al 2016;Onose et al 2016;Onose et al 2017).…”

Section: Introductionmentioning

confidence: 84%

Data multiplexing in radio interferometric calibration

Yatawatta

Diblen

Spreeuw

et al. 2017

Monthly Notices of the Royal Astronomical Society

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New and upcoming radio interferometers will produce unprecedented amounts of data that demand extremely powerful computers for processing. This is a limiting factor due to the large computational power and energy costs involved. Such limitations restrict several key data processing steps in radio interferometry. One such step is calibration where systematic errors in the data are determined and corrected. Accurate calibration is an essential component in reaching many scientific goals in radio astronomy and the use of consensus optimization that exploits the continuity of systematic errors across frequency significantly improves calibration accuracy. In order to reach full consensus, data at all frequencies need to be calibrated simultaneously. In the SKA regime, this can become intractable if the available compute agents do not have the resources to process data from all frequency channels simultaneously. In this paper, we propose a multiplexing scheme that is based on the alternating direction method of multipliers (ADMM) with cyclic updates. With this scheme, it is possible to simultaneously calibrate the full dataset using far fewer compute agents than the number of frequencies at which data are available. We give simulation results to show the feasibility of the proposed multiplexing scheme in simultaneously calibrating a full dataset when a limited number of compute agents are available.

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“…Our analysis (which is an extension of our previous work (Yatawatta 2018)) is general and can easily be adopted for specific calibration scenarios such as traditional calibration (without using consensus) or calibration using a scalar data model (single polarization) or direction independent calibration. Furthermore, the same method of analysis can be used to study the performance of other data processing steps in radio interferometry such as imaging (Onose et al 2016;Meillier et al 2016;Deguignet et al 2016;Onose et al 2017) or foreground removal (Chapman et al 2013;Trott et al 2016;Mertens et al 2018). In addition, the same analysis can be applied to other machine learning problems as well, for instance to study over fitting (Tetko et al 1995;Srivastava et al 2014).…”

Section: Introductionmentioning

confidence: 99%

Statistical performance of radio interferometric calibration

Yatawatta

2019

Monthly Notices of the Royal Astronomical Society

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Calibration is an essential step in radio interferometric data processing that corrects the data for systematic errors and in addition, subtracts bright foreground interference to reveal weak signals hidden in the residual. These weak and unknown signals are much sought after to reach many science goals but the effect of calibration on such signals is an ever present concern. The main reason for this is the incompleteness of the model used in calibration. Distributed calibration based on consensus optimization has been shown to mitigate the effect due to model incompleteness by calibrating data covering a wide bandwidth in a computationally efficient manner. In this paper, we study the statistical performance of direction dependent distributed calibration, i.e., the distortion caused by calibration on the residual statistics. In order to study this, we consider the mapping between the input uncalibrated data and the output residual data. We derive an analytical relationship for the influence of the input on the residual and use this to find the relationship between the input and output probability density functions. The eigenspectrum of the Jacobian of this mapping is a direct indicator of the statistical performance of calibration. The analysis developed in this paper can also be applied to other data processing steps in radio interferometry such as imaging and foreground subtraction as well as to many other machine learning problems.

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Two distributed algorithms for the deconvolution of large radio-interferometric multispectral images

Cited by 7 publications

References 5 publications

Distribution strategies for very large 3D image deconvolution algorithms

Distribution strategies for very large 3D image deconvolution algorithms

Data multiplexing in radio interferometric calibration

Statistical performance of radio interferometric calibration

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