Radio Interferometric Imaging Research Articles

Multi-step reconstruction of radio-interferometric images

The large aperture arrays for the currently under construction SKA Observatory (SKAO) will allow for observations of the universe in the radio spectrum at unprecedented resolution and sensitivity. However, these telescopes will produce data on the scale of exabytes, introducing a slew of hardware and software design challenges. This paper proposes a multi-step image reconstruction framework that allows for partitioning of visibility data by baseline length. This enables more flexible data distribution and parallelization, aiding in processing radio-astronomical observations within given constraints. Additionally, as each step of the framework only relies on a subset of the total visibilities, one can perform reconstruction progressively, with the initial step performed on the SKAO Science Data Processors and the second on local clusters. The multi-step reconstruction is separated into two steps. First a low-resolution image is reconstructed with only short-baseline visibilities, and then using this image together with the long-baseline visibilities, the full-resolution image is reconstructed. The proposed method only operates in the minor cycle, and it can be easily integrated into existing imaging pipelines. We show that our proposed method allows for partitioning of visibilities by baseline without introducing significant additional drawbacks, reconstructing images of similar quality within similar numbers of major cycles compared to a single-step all-baselines approach that uses the same reconstruction method as well as compared to multi-scale CLEAN.

Fast-resolve : Fast Bayesian radio interferometric imaging

fast-resolve : Fast Bayesian radio interferometric imaging

Radio U-Net: a convolutional neural network to detect diffuse radio sources in galaxy clusters and beyond

ABSTRACT The forthcoming generation of radio telescope arrays promises significant advancements in sensitivity and resolution, enabling the identification and characterization of many new faint and diffuse radio sources. Conventional manual cataloguing methodologies are anticipated to be insufficient to exploit the capabilities of new radio surveys. Radio interferometric images of diffuse sources present a challenge for image segmentation tasks due to noise, artifacts, and embedded radio sources. In response to these challenges, we introduce Radio U-Net, a fully convolutional neural network based on the U-Net architecture. Radio U-Net is designed to detect faint and extended sources in radio surveys, such as radio haloes, relics, and cosmic web filaments. Radio U-Net was trained on synthetic radio observations built upon cosmological simulations and then tested on a sample of galaxy clusters, where the detection of cluster diffuse radio sources relied on customized data reduction and visual inspection of Low-Frequency Array Two metre Sky Survey (LoTSS) data. The 83 per cent of clusters exhibiting diffuse radio emission were accurately identified, and the segmentation successfully recovered the morphology of the sources even in low-quality images. In a test sample comprising 246 galaxy clusters, we achieved a 73 per cent accuracy rate in distinguishing between clusters with and without diffuse radio emission. Our results establish the applicability of Radio U-Net to extensive radio survey data sets, probing its efficiency on cutting-edge high-performance computing systems. This approach represents an advancement in optimizing the exploitation of forthcoming large radio surveys for scientific exploration.

Scalable Bayesian uncertainty quantification with data-driven priors for radio interferometric imaging

Abstract Next-generation radio interferometers like the Square Kilometer Array have the potential to unlock scientific discoveries thanks to their unprecedented angular resolution and sensitivity. One key to unlocking their potential resides in handling the deluge and complexity of incoming data. This challenge requires building radio interferometric (RI) imaging methods that can cope with the massive data sizes and provide high-quality image reconstructions with uncertainty quantification (UQ). This work proposes a method coined QuantifAI to address UQ in RI imaging with data-driven (learned) priors for high-dimensional settings. Our model, rooted in the Bayesian framework, uses a physically motivated model for the likelihood. The model exploits a data-driven convex prior potential, which can encode complex information learned implicitly from simulations and guarantee the log-concavity of the posterior. We leverage probability concentration phenomena of high-dimensional log-concave posteriors to obtain information about the posterior, avoiding MCMC sampling techniques. We rely on convex optimisation methods to compute the MAP estimation, which is known to be faster and better scale with dimension than MCMC strategies. QuantifAI allows us to compute local credible intervals and perform hypothesis testing of structure on the reconstructed image. We propose a novel fast method to compute pixel-wise uncertainties at different scales, which uses 3 and 6 orders of magnitude less likelihood evaluations than other UQ methods like length of the credible intervals and Monte Carlo posterior sampling, respectively. We demonstrate our method by reconstructing RI images in a simulated setting and carrying out fast and scalable UQ, which we validate with MCMC sampling. Our method shows an improved image quality and more meaningful uncertainties than the benchmark method based on a sparsity-promoting prior. QuantifAI’s source code is available from https://github.com/astro-informatics/QuantifAI.

The R2D2 Deep Neural Network Series Paradigm for Fast Precision Imaging in Radio Astronomy

Radio-interferometric imaging entails solving high-resolution high-dynamic-range inverse problems from large data volumes. Recent image reconstruction techniques grounded in optimization theory have demonstrated remarkable capability for imaging precision, well beyond CLEAN’s capability. These range from advanced proximal algorithms propelled by handcrafted regularization operators, such as the SARA family, to hybrid plug-and-play (PnP) algorithms propelled by learned regularization denoisers, such as AIRI. Optimization and PnP structures are however highly iterative, which hinders their ability to handle the extreme data sizes expected from future instruments. To address this scalability challenge, we introduce a novel deep-learning approach, dubbed “Residual-to-Residual DNN series for high-Dynamic-range imaging” or in short R2D2. R2D2's reconstruction is formed as a series of residual images, iteratively estimated as outputs of deep neural networks (DNNs) taking the previous iteration’s image estimate and associated data residual as inputs. It thus takes a hybrid structure between a PnP algorithm and a learned version of the matching pursuit algorithm that underpins CLEAN. We present a comprehensive study of our approach, featuring its multiple incarnations distinguished by their DNN architectures. We provide a detailed description of its training process, targeting a telescope-specific approach. R2D2's capability to deliver high precision is demonstrated in simulation, across a variety of image and observation settings using the Very Large Array. Its reconstruction speed is also demonstrated: with only a few iterations required to clean data residuals at dynamic ranges up to 105, R2D2 opens the door to fast precision imaging. R2D2 codes are available in the BASPLib (https://basp-group.github.io/BASPLib/) library on GitHub.

Low-frequency Radio Interferometric Array Imaging Pipeline Optimization Based on Distributed Execution Framework

The Square Kilometre Array (SKA) project is an international collaboration to build the world’s largest radio telescope, whose sensitivity and measurement speed will be an order of magnitude higher than those of all current radio telescopes. Radio continuum survey is one of the main observation mode of the SKA, and the establishment of a standard map of the survey area based on continuum imaging will provide an important foundation for subsequent astronomical science. The GaLactic and Extragalactic All-sky Murchison Widefield Array survey eXtended (GLEAM-X) is a project of the SKA pilot telescope Murchison Widefield Array (MWA) in 2018—2020. GLEAM-X is a new radio continuum survey project to be carried out with the MWA Phase II expansion array in 2018—2020. The experience of optimizing the imaging pipeline based on the distributed execution framework will help to solve the problem of massive data processing. In this paper, we describe the process steps of GLEAM-X imaging pipeline, integrate and improve it, and realize parallel processing of multiple pipelines on the China SKA Regional Centre Prototype (CSRC-P), and verify the deployment and test the correctness of the imaging pipeline system using GLEAM-X observation data. The GLEAM-X observations were used to validate the deployment of the imaging pipeline system and test its correctness. Then, to optimize the pipelines and improve the processing efficiency, the Data Activated Liu Graph Engine (DALiuGE) was used to integrate the imaging pipelines into the DALiuGE execution framework to automate the distributed parallel processing of the pipelines. Performance tests and results’ analysis show that the optimized imaging pipeline based on the DALiuGE execution framework has better performance, more flexible adaptability, and scalability than the traditional parallel approach, and can support future large-scale continuum imaging experiments during the first phase of SKA commissioning.

Regularized maximum likelihood estimation for radio interferometric imaging in the presence of radiofrequency interferences

We consider a regularized Maximum Likelihood Estimation (MLE) framework to produce images in the context of radio interferometric measurements. Specifically, we consider the class of compound Gaussian distributions to model the additive noise in the presence of radiofrequency interferences. In most cases, direct maximization of the likelihood is not tractable. To overcome this issue, we propose a generic expectation–maximization (EM) algorithm in the presence of a compound Gaussian noise. In addition, we leverage an approximation of the forward radio interferometric operator to derive an original latent data space that allows the use of the FFT in the maximization step, leading to an accelerated extension of the proposed imaging algorithm. The proposed approaches are evaluated on simulated and real data and show a significant improvement in the robustness to the presence of radiofrequency interferences (RFI) in the measurement.

Milliarcsecond-scale radio structure of the most distant BL Lac object candidate at redshift 6.57

Context. The existence of accreting supermassive black holes of up to billions of solar masses at early cosmological epochs (in the context of this work, redshifts z ≳ 6) requires very fast growth rates that are challenging to explain. The presence of a relativistic jet can be a direct indication of activity and accretion status in active galactic nuclei (AGN), constraining the radiative properties of these extreme objects. However, known jetted AGN beyond z ∼ 6 are still very rare. Aims. The radio-emitting AGN J2331+1129 has recently been claimed as a candidate BL Lac object at redshift z = 6.57 based on its synchrotron-dominated emission spectrum and a lack of ultraviolet or optical emission lines. It is a promising candidate for the highest-redshift blazar known to date. The aim of the observations described here is to support or refute the blazar classification of this peculiar source. Methods. We performed high-resolution radio interferometric imaging observations of J2331+1129 using the Very Long Baseline Array at 1.6 and 4.9 GHz in February 2022. Results. The images reveal a compact but slightly resolved, flat-spectrum core feature at both frequencies, indicating that the total radio emission is produced by a compact jet and originates from within a central region of ∼10 pc in diameter. While these details are consistent with the radio properties of a BL Lac object, the inferred brightness temperatures are at least an order of magnitude lower than expected for a Doppler-boosted radio jet, which casts doubt on the high-redshift BL Lac identification.

Correlation structure in flux-density calibrator models

ABSTRACT Astronomical radio-interferometric imaging typically uses published observations of standard flux-calibrator sources to calibrate the spectral response of the instrument. The spectra of standard flux-calibrator sources are usually provided as polynomial models of flux-density as a function of frequency. In this paper we show that there is significant covariance in these polynomial coefficients, and that failing to take this into account leads to significantly larger variance when sampling from the polynomial models. This paper presents polynomial models of calibrator sources that include the covariance structure of the coefficients which are computed using Markov Chain Monte Carlo sampling. In addition a data-free inference technique is presented that can be used to estimate the covariance structure from a simple polynomial model when access to the original data is not available. This data-free technique is compared with estimates of covariance calculated from original observation data. The data-free technique is shown to provide reasonable agreement with covariances calculated from source data. A python package is described that implements this inference, and a catalogue of common flux-calibrator models including covariance is provided. We suggest that when polynomial models of flux-calibrators are used as priors in a Bayesian context, then taking this correlation structure into account will lead to significantly reduced posterior variance.

Fast grid to grid interpolation for radio interferometric imaging

Backward and forward interpolations on a Fourier grid are computationally expensive operations for radio interferometric imaging algorithms. By merging these operations, we propose the Grid to Grid (G2G) method which aims to reduce the computational cost and memory footprint. We have also shown that the oversampling factor used for the convolution function in the G2G method strongly impacted the accuracy and computational speed. Acceleration on graphics processing units (GPU), well suited for this embarrassingly parallel algorithm, has been studied mainly for backward operation. Thus, we propose a GPU and CPU implementation of the G2G method on Nvidia A100 and Intel Ice Lake processors. Experiments have shown a GPU performance improvement with a Fourier-point throughput better than up to 37% regarding standard gridder and degridder.

Bayesian radio interferometric imaging with direction-dependent calibration

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.

Deep-learning-based radiointerferometric imaging with GAN-aided training

Context. The incomplete coverage of the spatial Fourier space, which leads to imaging artifacts, has been troubling radio interferometry for a long time. The currently best technique is to create an image for which the visibility data are Fourier-transformed and to clean the systematic effects originating from incomplete data in Fourier space. We have shown previously how super-resolution methods based on convolutional neural networks can reconstruct sparse visibility data. Aims. The training data in our previous work were not very realistic. The aim of this work is to build a whole simulation chain for realistic radio sources that then leads to an improved neural net for the reconstruction of missing visibilities. This method offers considerable improvements in terms of speed, automatization, and reproducibility over the standard techniques. Methods. We generated large amounts of training data by creating images of radio galaxies with a generative adversarial network that was trained on radio survey data. Then, we applied the radio interferometer measurement equation in order to simulate the measurement process of a radio interferometer. Results. We show that our neural network can faithfully reconstruct images of realistic radio galaxies. The reconstructed images agree well with the original images in terms of the source area, integrated flux density, peak flux density, and the multiscale structural similarity index. Finally, we show that the neural net can be adapted for estimating the uncertainties in the imaging process.

Scalable precision wide-field imaging in radio interferometry – II. AIRI validated on ASKAP data

ABSTRACT Accompanying Part I, this sequel delineates a validation of the recently proposed AI for Regularization in radio-interferometric Imaging (AIRI) algorithm on observations from the Australian Square Kilometre Array Pathfinder (ASKAP). The monochromatic AIRI-ASKAP images showcased in this work are formed using the same parallelized and automated imaging framework described in Part I: ‘uSARA validated on ASKAP data’. Using a Plug-and-Play approach, AIRI differs from uSARA by substituting a trained denoising deep neural network (DNN) for the proximal operator in the regularization step of the forward–backward algorithm during deconvolution. We build a trained shelf of DNN denoisers that target the estimated image dynamic ranges of our selected data. Furthermore, we quantify variations of AIRI reconstructions when selecting the nearest DNN on the shelf versus using a universal DNN with the highest dynamic range, opening the door to a more complete framework that not only delivers image estimation but also quantifies epistemic model uncertainty. We continue our comparative analysis of source structure, diffuse flux measurements, and spectral index maps of selected target sources as imaged by AIRI and the algorithms in Part I – uSARA and WSClean. Overall, we see an improvement over uSARA and WSClean in the reconstruction of diffuse components in AIRI images. The scientific potential delivered by AIRI is evident in further imaging precision, more accurate spectral index maps, and a significant acceleration in deconvolution time, whereby AIRI is four times faster than its subiterative sparsity-based counterpart uSARA.

Multimessenger Picture of J1048+7143* *Released xxxx 2022.

We draw a multimessenger picture of J1048+7143, a flat-spectrum radio quasar known to show quasiperiodic oscillations in the γ-ray regime. We generate the adaptively binned Fermi Large Area Telescope light curve of this source above 168 MeV to find three major γ-ray flares of the source, such that each of the three flares consists of two sharp subflares. Based on radio interferometric imaging data taken with the Very Large Array, we find that the kiloparsec-scale jet is directed west, while our analysis of 8.6 GHz very long baseline interferometry data, mostly taken with the Very Long Baseline Array, revealed signatures of two parsec-scale jets, one pointing east, one pointing south. We suggest that the misalignment of the kiloparsec- and parsec-scale jets is a revealing signature of jet precession. We also analyze the 5 GHz total flux density curve of J1048+7143 taken with the Nanshan (Ur) and RATAN-600 single-dish radio telescopes and find two complete radio flares, lagging slightly behind the γ-ray flares. We model the timing of γ-ray flares as a signature of the spin–orbit precession in a supermassive black hole binary, and find that the binary could merge in the next ∼60–80 yr. We show that both pulsar timing arrays and the planned Laser Interferometer Space Antenna lack sensitivity and frequency coverage to detect the hypothetical supermassive black hole binary in J1048+7143. We argue that the identification of sources similar to J1048+7143 plays a key role in revealing periodic high-energy sources in the distant universe.

First AI for Deep Super-resolution Wide-field Imaging in Radio Astronomy: Unveiling Structure in ESO 137-006

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 regularizer in an optimization 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 low-dimensional blocks, enabling scalability to large data and image dimensions. We validate our framework for image formation at a wide field of view containing ESO 137-006 from 19 GB 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.

Image reconstruction algorithms in radio interferometry: From handcrafted to learned regularization denoisers

ABSTRACTWe introduce a new class of iterative image reconstruction algorithms for radio interferometry, at the interface of convex optimization and deep learning, inspired by plug-and-play methods. The approach consists in learning a prior image model by training a deep neural network (DNN) as a denoiser, and substituting it for the handcrafted proximal regularization operator of an optimization algorithm. The proposed AIRI (‘AI for Regularization in radio-interferometric Imaging’) framework, for imaging complex intensity structure with diffuse and faint emission from visibility data, inherits the robustness and interpretability of optimization, and the learning power and speed of networks. Our approach relies on three steps. First, we design a low dynamic range training data base from optical intensity images. Secondly, we train a DNN denoiser at a noise level inferred from the signal-to-noise ratio of the data. We use training losses enhanced with a non-expansiveness term ensuring algorithm convergence, and including on-the-fly data base dynamic range enhancement via exponentiation. Thirdly, we plug the learned denoiser into the forward–backward optimization algorithm, resulting in a simple iterative structure alternating a denoising step with a gradient-descent data-fidelity step. We have validated AIRI against clean, optimization algorithms of the SARA family, and a DNN trained to reconstruct the image directly from visibility data. Simulation results show that AIRI is competitive in imaging quality with SARA and its unconstrained forward–backward-based version uSARA, while providing significant acceleration. clean remains faster but offers lower quality. The end-to-end DNN offers further acceleration, but with far lower quality than AIRI.

Deep learning-based imaging in radio interferometry

Context.The sparse layouts of radio interferometers result in an incomplete sampling of the sky in Fourier space which leads to artifacts in the reconstructed images. Cleaning these systematic effects is essential for the scientific use of radiointerferometric images.Aims.Established reconstruction methods are often time-consuming, require expert knowledge, and suffer from a lack of reproducibility. We have developed a prototype deep learning-based method that generates reproducible images in an expedient fashion.Methods.To this end, we take advantage of the efficiency of convolutional neural networks to reconstruct image data from incomplete information in Fourier space. The neural network architecture is inspired by super-resolution models that utilize residual blocks. Using simulated data of radio galaxies that are composed of Gaussian components, we trained deep learning models whose reconstruction capability is quantified using various measures.Results.The reconstruction performance is evaluated on clean and noisy input data by comparing the resulting predictions with the true source images. We find that source angles and sizes are well reproduced, while the recovered fluxes show substantial scatter, albeit not worse than existing methods without fine-tuning. Finally, we propose more advanced approaches using deep learning that include uncertainty estimates and a concept to analyze larger images.

Tackling the Unique Challenges of Low-frequency Solar Polarimetry with the Square Kilometre Array Low Precursor: The Algorithm

Coronal magnetic fields are well known to be one of the crucial parameters defining coronal physics and space weather. However, measuring the global coronal magnetic fields remains challenging. The polarization properties of coronal radio emissions are sensitive to coronal magnetic fields. While they can prove to be useful probes of coronal and heliospheric magnetic fields, their usage has been limited by technical and algorithmic challenges. We present a robust algorithm for precise polarization calibration and imaging of low-radio frequency solar observations and demonstrate it on data from the Murchison Widefield Array, a Square Kilometre Array (SKA) precursor. This algorithm is based on the Measurement Equation framework, which forms the basis of all modern radio interferometric calibration and imaging. It delivers high-dynamic-range and high-fidelity full-Stokes solar radio images with instrumental polarization leakages <1%, on par with general astronomical radio imaging, and represents the state of the art. Opening up this rewarding, yet unexplored, phase space will enable multiple novel science investigations and offer considerable discovery potential. Examples include detection of low-level circular polarization from thermal coronal emission to estimate large-scale quiescent coronal fields; polarization of faint gyrosynchrotron emissions from coronal mass ejections for robust estimation of plasma parameters; and detection of the first-ever linear polarization at these frequencies. This method has been developed with the SKA in mind and will enable a new era of high-fidelity spectropolarimetric snapshot solar imaging at low radio frequencies.

Deep radio-interferometric imaging with POLISH: DSA-2000 and weak lensing

ABSTRACT Radio interferometry allows astronomers to probe small spatial scales that are often inaccessible with single-dish instruments. However, recovering the radio sky from an interferometer is an ill-posed deconvolution problem that astronomers have worked on for half a century. More challenging still is achieving resolution below the array’s diffraction limit, known as superresolution imaging. To this end, we have developed a new learning-based approach for radio interferometric imaging, leveraging recent advances in the classical computer vision problems of single-image superresolution and deconvolution. We have developed and trained a high-dynamic range residual neural network to learn the mapping between the dirty image and the true radio sky. We call this procedure POLISH, in contrast to the traditional CLEAN algorithm. The feed-forward nature of learning-based approaches like POLISH is critical for analysing data from the upcoming Deep Synoptic Array (DSA-2000). We show that POLISH achieves superresolution, and we demonstrate its ability to deconvolve real observations from the Very Large Array. Superresolution on DSA-2000 will allow us to measure the shapes and orientations of several hundred million star-forming radio galaxies (SFGs), making it a powerful cosmological weak lensing survey and probe of dark energy. We forecast its ability to constrain the lensing power spectrum, finding that it will be complementary to next-generation optical surveys such as Euclid.

Variable structure in the PDS 70 disc and uncertainties in radio-interferometric image restoration

ABSTRACT The compact mm-wavelength signal in the central cavity of the PDS 70 disc, revealed by deep ALMA observations, is aligned with unresolved H α emission, and is thought to stem from a circumplanetary disc (CPD) around PDS 70c. We revisit the available ALMA data on PDS 70c with alternative imaging strategies, and with special attention to uncertainties and to the impact of the so-called ‘JvM correction’, which is thought to improve the dynamic range of restored images. We also propose a procedure for the alignment and joint imaging of multi-epoch visibility data. We find that the JvM correction exaggerates the peak signal-to-noise of the data, by up to a factor of 10. In the case of PDS 70, we recover the detection of PDS 70c from the 2019 July data, but only at 8 σ. However, its non-detection in 2017 Dec. suggests that PDS 70c is variable by at least $42{{\ \rm per\ cent}} \pm 13{{\ \rm per\ cent}}$ over a 1.75 yr time-span, so similar to models of the H α variability. We also pick up fine structure in the inner disc, such that its peak is offset by ∼0${_{.}^{\prime\prime}}$04 from the disc centre. The inner disc is variable too, which we tentatively ascribe to Keplerian rotation as well as intrinsic morphological changes.