Cygnus A jointly calibrated and imaged via non-convex optimization from VLA data

Dabbech, Arwa; Repetti, Audrey; Perley, R. A.; Smirnov, O.; Wiaux, Yves

doi:10.1093/mnras/stab1903

Cited by 11 publications

(40 citation statements)

References 35 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Firstly, these artefacts take the form of structures emanating from the hotspots in the model images of Faceted HyperSARA, and ringing artefacts around the hotspots in WSClean images. Similar structures were observed in reconstructions of Cyg A at similar frequencies (Dabbech et al 2021) and were attributed to pointing errors. Secondly, structures around the core of the radio galaxy are observed in the low frequency-channel image recovered by Faceted HyperSARA (top row of Figure 4) and WSClean reconstructed images at both channels (bottom rows of Figures 4-5).…”

Section: Imaging Qualitysupporting

confidence: 74%

“…More specifically, models of the RI measurement operator can encapsulate the 𝑤-term, a direction-dependent effect resulting from the non-coplanarity of the radio array, via 𝑤-projection (Cornwell 2008;Wolz et al 2013;Dabbech et al 2017). Available direction-dependent effect solutions of other origins (e.g., atmospheric or instrumental) obtained from a ★ E-mail: y.wiaux@hw.ac.uk calibration pre-processing step (Repetti et al 2017;Dabbech et al 2021), can also be integrated into the measurement operator. Furthermore, unknown effective noise levels induced by the imperfect calibration are estimated using an adaptive strategy inspired from Dabbech et al (2018).…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Parallel faceted imaging in radio interferometry via proximal splitting (Faceted HyperSARA): II. Code and real data proof of concept

Thouvenin¹,

Dabbech²,

Jiang³

et al. 2022

Preprint

View full text Add to dashboard Cite

In a companion paper, a faceted wideband imaging technique for radio interferometry, dubbed Faceted HyperSARA, has been introduced and validated on synthetic data. Building on the recent HyperSARA approach, Faceted HyperSARA leverages the splitting functionality inherent to the underlying primal-dual forward-backward algorithm to decompose the image reconstruction over multiple spatio-spectral facets. The approach allows complex regularization to be injected into the imaging process while providing additional parallelization flexibility compared to HyperSARA. The present paper introduces new algorithm functionalities to address real datasets, implemented as part of a fully fledged MATLAB imaging library made available on G. A large scale proof-of-concept is proposed to validate Faceted HyperSARA in a new data and parameter scale regime, compared to the state-of-the-art. The reconstruction of a 15 GB wideband image of Cyg A from 7.4 GB of VLA data is considered, utilizing 1440 CPU cores on a HPC system for about 9 hours. The conducted experiments illustrate the reconstruction performance of the proposed approach on real data, exploiting new functionalities to set, both an accurate model of the measurement operator accounting for known direction-dependent effects (DDEs), and an effective noise level accounting for imperfect calibration. They also demonstrate that, when combined with a further dimensionality reduction functionality, Faceted HyperSARA enables the recovery of a 3.6 GB image of Cyg A from the same data using only 91 CPU cores for 39 hours. In this setting, the proposed approach is shown to provide a superior reconstruction quality compared to the state-of-the-art wideband CLEAN-based algorithm of the WSClean software.

show abstract

Section: Imaging Qualitysupporting

confidence: 74%

Section: Introductionmentioning

confidence: 99%

Parallel faceted imaging in radio interferometry via proximal splitting (Faceted HyperSARA): II. Code and real data proof of concept

Thouvenin¹,

Dabbech²,

Jiang³

et al. 2022

Preprint

View full text Add to dashboard Cite

show abstract

“…Combining 𝑤-stacking and 𝑤projection results in a memory-efficient, and accurate measurement operator. We also emphasise that any DDE calibration solutions modelled as Fourier kernels can be easily injected in the measurement operator model via further row-based convolution (Dabbech et al 2021).…”

Section: Methodsmentioning

confidence: 99%

“…No such consideration is necessary for uSARA and AIRI, where the algorithm solution itself, without further processing, is considered to be the final image reconstruction. This advantage was already highlighted for the previous algorithms of the SARA family, which rely on more advanced and physical regularisation models than CLEAN (Dabbech et al 2018;Abdulaziz et al 2019;Dabbech et al 2021;Thouvenin et al 2022b). By construction, uSARA and AIRI images are in units of Jy/pixel.…”

Section: Mainmentioning

confidence: 99%

“…Li et al 2011;Carrillo et al 2012;Dabbech et al 2015;Garsden et al 2015). In particular, the SARA family of algorithms (Onose et al 2017;Repetti et al 2017;Dabbech et al 2018;Abdulaziz et al 2019;Dabbech et al 2021;Thouvenin et al 2022a,b) have recently delivered a significant increase of resolution and dynamic range (or depth) over CLEAN-based algorithms (e.g. Högbom 1974; Wakker & Schwarz 1988;Cornwell 2008) on the well known radio galaxy Cygnus A. Owing to the complexity of the regularisation models underpinning the imaging accuracy, optimisation approaches are significantly more computationally expensive than CLEAN.…”

Section: Mainmentioning

confidence: 99%

See 1 more Smart Citation

First AI for deep super-resolution wide-field imaging in radio astronomy: unveiling structure in ESO 137--006

Dabbech¹,

Terris²,

Jackson³

et al. 2022

Preprint

View full text Add to dashboard Cite

We introduce the first AI-based framework for deep, super-resolution, wide-field radio-interferometric imaging, and demonstrate it on observations of the ESO 137-006 radio galaxy. The algorithmic framework to solve the inverse problem for image reconstruction builds on a recent "plug-and-play" scheme whereby a denoising operator is injected as an image regulariser in an optimisation algorithm, which alternates until convergence between denoising steps and gradient-descent data-fidelity steps. We investigate handcrafted and learned variants of high-resolution high-dynamic range denoisers. We propose a parallel algorithm implementation relying on automated decompositions of the image into facets, and the measurement operator into sparse lowdimensional blocks. The resulting algorithms were deployed to form images of a wide field of view containing ESO 137-006, from 19 gigabytes of MeerKAT data at 1053 and 1399 MHz. The recovered maps exhibit significantly more resolution and dynamic range than CLEAN, revealing collimated synchrotron threads close to the galactic core.

show abstract

Bayesian radio interferometric imaging with direction-dependent calibration

Roth,

Arras,

Reinecke

et al. 2023

A&A

View full text Add to dashboard Cite

Context. Radio interferometers measure frequency components of the sky brightness, modulated by the gains of the individual radio antennas. Due to atmospheric turbulence and variations in the operational conditions of the antennas, these gains fluctuate. Thereby the gains do not only depend on time, but also on the spatial direction on the sky. To recover high-quality radio maps, an accurate reconstruction of the direction and time-dependent individual antenna gains is required. Aims. This paper aims to improve the reconstruction of radio images, by introducing a novel joint imaging and calibration algorithm including direction-dependent antenna gains. Methods. Building on the resolve framework, we designed a Bayesian imaging and calibration algorithm utilizing the image domain gridding method for numerically efficient application of direction-dependent antenna gains. Furthermore, by approximating the posterior probability distribution with variational inference, our algorithm can provide reliable uncertainty maps. Results. We demonstrate the ability of the algorithm to recover high resolution high dynamic range radio maps from VLA data of the radio galaxy Cygnus A. We compare the quality of the recovered images with previous work relying on classically calibrated data. Furthermore, we compare the results with a compressed sensing algorithm also incorporating direction-dependent gains. Conclusions. Including direction-dependent effects in the calibration model significantly improves the dynamic range of the reconstructed images compared to reconstructions from classically calibrated data. Compared to the compressed sensing reconstruction, the resulting sky images have a higher resolution and show fewer artifacts. For utilizing the full potential of radio interferometric data, it is essential to consider the direction dependence of the antenna gains.

show abstract

Cygnus A jointly calibrated and imaged via non-convex optimization from VLA data

Cited by 11 publications

References 35 publications

Parallel faceted imaging in radio interferometry via proximal splitting (Faceted HyperSARA): II. Code and real data proof of concept

Parallel faceted imaging in radio interferometry via proximal splitting (Faceted HyperSARA): II. Code and real data proof of concept

First AI for deep super-resolution wide-field imaging in radio astronomy: unveiling structure in ESO 137--006

Bayesian radio interferometric imaging with direction-dependent calibration

Contact Info

Product

Resources

About