Cygnus A super-resolved via convex optimization from VLA data

Dabbech, Arwa; Onose, Alexandru; Abdulaziz, Abdullah; Perley, R. A.; Smirnov, O.; Wiaux, Yves

doi:10.1093/mnras/sty372

Cited by 38 publications

(44 citation statements)

References 45 publications

(104 reference statements)

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“…The recent discovery of a further supermassive black hole candidate in Cygnus A (Cyg A-2 Perley et al 2017;Dabbech et al 2018) cannot be related to the precession signatures discussed before. Cyg A-2 is located 460 pc away from the primary AGN (Cyg A-1) in projection, too far away to cause a detectable geodetic precession.…”

Section: The Case For a Supermassive Black Hole Binary In Cygnus Amentioning

confidence: 91%

How frequent are close supermassive binary black holes in powerful jet sources?

Krause

Shabala

Hardcastle

et al. 2018

Monthly Notices of the Royal Astronomical Society

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Supermassive black hole binaries may be detectable by an upcoming suite of gravitational wave experiments. Their binary nature can also be revealed by radio jets via a short-period precession driven by the orbital motion as well as the geodetic precession at typically longer periods. We have investigated Karl G. Jansky Very Large Array (VLA) and MERLIN radio maps of powerful jet sources for morphological evidence of geodetic precession. For perhaps the best studied source, Cygnus A, we find strong evidence for geodetic precession. Projection effects can enhance precession features, for which we find indications in strongly projected sources. For a complete sample of 33 3CR radio sources we find strong evidence for jet precession in 24 cases (73 per cent). The morphology of the radio maps suggests that the precession periods are of the order of 10 6 − 10 7 yr. We consider different explanations for the morphological features and conclude that geodetic precession is the best explanation. The frequently observed gradual jet angle changes in samples of powerful blazars can be explained by orbital motion. Both observations can be explained simultaneously by postulating that a high fraction of powerful radio sources have sub-parsec supermassive black hole binaries. We consider complementary evidence and discuss if any jetted supermassive black hole with some indication of precession could be detected as individual gravitational wave source in the near future. This appears unlikely, with the possible exception of M87.

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Section: The Case For a Supermassive Black Hole Binary In Cygnus Amentioning

confidence: 91%

How frequent are close supermassive binary black holes in powerful jet sources?

Krause

Shabala

Hardcastle

et al. 2018

Monthly Notices of the Royal Astronomical Society

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show abstract

“…These are the saturation of the model cube estimate, reflected by β (t) = X (t) − X (t−1) 2 X (t) 2 being below a low value λ 1 set by the user and a minimum number of iterations between consecutive updates is performed. An overview of the variables and parameters associated with the adaptive strategy is provided in Appendix A (see Dabbech et al (2018) for more details).…”

Section: Adaptive 2 Bounds Adjustmentmentioning

confidence: 99%

“…Wiaux et al 2009;Carrillo et al 2012;Dabbech et al 2015) and real data applications (e.g. Wenger et al 2010;Garsden et al 2015;Pratley et al 2017;Onose et al 2017;Dabbech et al 2018) for single channel RI imaging. We also provide a spectral analysis of selected pixels from the different sources of the estimated wideband cubes.…”

Section: Benchmark Algorithms and Imaging Quality Assessmentmentioning

confidence: 99%

Wideband super-resolution imaging in Radio Interferometry via low rankness and joint average sparsity models (HyperSARA)

Abdulaziz

Dabbech

Wiaux

2019

Monthly Notices of the Royal Astronomical Society

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We propose a new approach within the versatile framework of convex optimization to solve the radio-interferometric wideband imaging problem. Our approach, dubbed HyperSARA, solves a sequence of weighted nuclear norm and 2,1 minimization problems promoting low rankness and joint average sparsity of the wideband model cube. On the one hand, enforcing low rankness enhances the overall resolution of the reconstructed model cube by exploiting the correlation between the different channels. On the other hand, promoting joint average sparsity improves the overall sensitivity by rejecting artefacts present on the different channels. An adaptive Preconditioned Primal-Dual algorithm is adopted to solve the minimization problem. The algorithmic structure is highly scalable to large data sets and allows for imaging in the presence of unknown noise levels and calibration errors. We showcase the superior performance of the proposed approach, reflected in high-resolution images on simulations and real VLA observations with respect to single channel imaging and the clean-based wideband imaging algorithm in the wsclean software. Our matlab code is available online on github.

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“…A greedy alternative to the minimization of a regularized cost function was proposed by Dabbech et al (2015). Sparse image representations using wavelet dictionaries has indisputably shown substantial improvement with respect to classical CLEAN-based approaches, as demonstrated by Garsden et al (2015) using LOFAR data and Dabbech et al (2018) using VLA data. The price to pay is an increased computational cost and a lot of effort has gone into designing scalable methods able to cope with large data sets.…”

Section: Introductionmentioning

confidence: 99%

A parallel and automatically tuned algorithm for multispectral image deconvolution

Ammanouil

Ferrari

Mary

et al. 2019

Monthly Notices of the Royal Astronomical Society

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In the era of big data in the radio astronomical field, image reconstruction algorithms are challenged to estimate clean images given limited computing resources and time. This article is driven by the extensive need for large scale image reconstruction for the future Square Kilometre Array (SKA), the largest low-and intermediate frequency radio telescope of the next decades. This work proposes a scalable wideband deconvolution algorithm called MUFFIN, which stands for "MUlti Frequency image reconstruction For radio INterferometry". MUFFIN estimates the sky images at various frequency bands given the corresponding dirty images and point spread functions. The reconstruction is achieved by minimizing a data fidelity term and joint spatial and spectral sparse analysis regularization terms. It is consequently non-parametric w.r.t. the spectral behaviour of radio sources. MUFFIN algorithm is endowed with a parallel implementation and an automatic tuning of the regularization parameters, making it scalable and well suited for big data applications such as SKA. Comparisons between MUFFIN and the state-of-the-art wideband reconstruction algorithm are provided.

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Cygnus A super-resolved via convex optimization from VLA data

Cited by 38 publications

References 45 publications

How frequent are close supermassive binary black holes in powerful jet sources?

How frequent are close supermassive binary black holes in powerful jet sources?

Wideband super-resolution imaging in Radio Interferometry via low rankness and joint average sparsity models (HyperSARA)

A parallel and automatically tuned algorithm for multispectral image deconvolution

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