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Modern large radio continuum surveys have high sensitivity and resolution, and can resolve previously undetected extended and diffuse emissions, which brings great challenges for the detection and morphological classification of extended sources. We present HeTu-v2, a deep learning-based source detector that uses the combined networks of Mask Region-based Convolutional Neural Networks (Mask R-CNN) and a Transformer block to achieve high-quality radio sources segmentation and classification. The sources are classified into 5 categories: Compact or point-like sources (CS), Fanaroff-Riley Type I (FRI), Fanaroff-Riley Type II (FRII), Head-Tail (HT), and Core-Jet (CJ) sources. HeTu-v2 has been trained and validated with the data from the Faint Images of the Radio Sky at Twenty-one centimeters (FIRST). We found that HeTu-v2 has a high accuracy with a mean average precision ($AP_{\rm @50:5:95}$) of 77.8%, which is 15.6 points and 11.3 points higher than that of HeTu-v1 and the original Mask R-CNN respectively. We produced a FIRST morphological catalog (FIRST-HeTu) using HeTu-v2, which contains 835,435 sources and achieves 98.6% of completeness and up to 98.5% of accuracy compared to the latest 2014 data release of the FIRST survey. HeTu-v2 could also be employed for other astronomical tasks like building sky models, associating radio components, and classifying radio galaxies., Comment: 20 pages, 15 figures, Accepted by Astronomy and Computing

Jets may have contributed to promoting the growth of seed black holes in the early Universe, and thus observations of radio-loud high-redshift quasars are crucial to understanding the growth and evolution of the early supermassive black holes. Here we report the radio properties of an X-ray bright $z=5.5$ quasar, SRGE J170245.3+130104 (J1702+1301). Our high-resolution radio images reveal the radio counterpart at the optical position of J1702+1301, while another radio component is also detected at $\sim$23.5\arcsec\ to the southwest. Our analysis suggests that this southwest component is associated with a foreground galaxy at $z\approx 0.677$, which is mixed with J1702+1301 in low-frequency low-resolution radio images. After removing the contamination from this foreground source, we recalculated the radio loudness of J1702+1301 to be $R>$1100, consistent with those of blazars. J1702+1301 exhibits a flat radio spectrum ($\alpha = -0.17 \pm 0.05$, $S \propto \nu^\alpha$) between 0.15 and 5 GHz; above 5 GHz, it shows a rising spectrum shape, and the spectral index $\alpha^{8.2}_{4.7}$ appears to be correlated with the variation of the flux density: in burst states, $\alpha^{8.2}_{4.7}$ becomes larger. J1702+1301 displays distinct radio variability on timescales from weeks to years in the source's rest frame. These radio properties, including high radio loudness, rising spectrum, and rapid variability, tend to support it as a blazar., Comment: accepted for publication in Monthly Notices of the Royal Astronomical Society

As the largest radio telescope in the world, the Square Kilometre Array (SKA) will lead the next generation of radio astronomy. The feats of engineering required to construct the telescope array will be matched only by the techniques developed to exploit the rich scientific value of the data. To drive forward the development of efficient and accurate analysis methods, we are designing a series of data challenges that will provide the scientific community with high-quality datasets for testing and evaluating new techniques. In this paper we present a description and results from the first such Science Data Challenge (SDC1). Based on SKA MID continuum simulated observations and covering three frequencies (560 MHz, 1400MHz and 9200 MHz) at three depths (8 h, 100 h and 1000 h), SDC1 asked participants to apply source detection, characterization and classification methods to simulated data. The challenge opened in November 2018, with nine teams submitting results by the deadline of April 2019. In this work we analyse the results for 8 of those teams, showcasing the variety of approaches that can be successfully used to find, characterise and classify sources in a deep, crowded field. The results also demonstrate the importance of building domain knowledge and expertise on this kind of analysis to obtain the best performance. As high-resolution observations begin revealing the true complexity of the sky, one of the outstanding challenges emerging from this analysis is the ability to deal with highly resolved and complex sources as effectively as the unresolved source population., Comment: 18 pages, 11 figures, accepted by MNRAS

The Square Kilometre Array (SKA) project consists of delivering two largest radio telescope arrays being built by the SKA Observatory (SKAO), which is an intergovernmental organization bringing together nations from around the world with China being one of the major member countries. The computing resources needed to process, distribute, curate and use the vast amount of data that will be generated by the SKA telescopes are too large for the SKAO to manage on its own. To address this challenge, the SKAO is working with the international community to create a shared, distributed data, computing and networking capability called the SKA Regional Centre Alliance. In this model, the SKAO will be supported by a global network of SKA Regional Centres (SRCs) distributed around the world in its member countries to build an end-to-end science data system that will provide astronomers with high-quality science products. SRCs undertake deep processing, scientific analysis, and long-term storage of the SKA data, as well as user support. China has been actively participating in and promoting the construction of SRCs. This paper introduces the international cooperation and ongoing prototyping of the global SRC network, the construction plan of the China SRC and describes in detail the China SRC prototype. The paper also presents examples of scientific applications of SKA precursor and pathfinder telescopes completed using resources from the China SRC prototype. Finally, the future prospects of the China SRC are presented., Comment: T. An, et al. Status and progress of China SKA Regional Centre prototype. Sci. China-Phys. Mech. Astron. 65: 129501 (2022)

Large surveys using modern telescopes are producing images that are increasing exponentially in size and quality. Identifying objects in the generated images by visual recognition is time-consuming and labor-intensive, while classifying the extracted radio sources is even more challenging. To address these challenges, we develop a deep learning-based radio source detector, named \textsc{HeTu}, which is capable of rapidly identifying and classifying radio sources in an automated manner for both compact and extended radio sources. \textsc{HeTu} is based on a combination of a residual network (ResNet) and feature pyramid network (FPN). We classify radio sources into four classes based on their morphology. The training images are manually labeled and data augmentation methods are applied to solve the data imbalance between the different classes. \textsc{HeTu} automatically locates the radio sources in the images and assigns them to one of the four classes. The experiment on the testing dataset shows an average operation time of 5.4 millisecond per image and a precision of 99.4\% for compact point-like sources and 98.1\% for double-lobe sources. We applied \textsc{HeTu} to the images obtained from the GaLactic and the Galactic Extragalactic All-Object Murchison Wide-field Array (GLEAM) survey project. More than 96.9\% of the \textsc{HeTu}-detected compact sources are matched compared to the source finding software used in the GLEAM. We also detected and classified 2,298 extended sources (including Fanaroff-Riley type I and II sources, and core-jet sources) above $5\sigma$. The cross-matching rates of extended sources are higher than 97\%, showing excellent performance of \textsc{HeTu} in identifying extended radio sources. \textsc{HeTu} provides an efficient tool for radio source finding and classification and can be applied to other scientific fields., Comment: Accepted by Science Bulletin. Note that the final version of the paper in the journal is formatted slightly differently from the one in astro-ph because of the journal's layout

w-Projection is a wide-field imaging technique that is widely used in radio synthesis arrays. Processing the wide-field big data generated by the future Square Kilometre Array (SKA) will require significant updates to current methods to significantly reduce the time consumed on data processing. Data loading and gridding are found to be two major time-consuming tasks in w-projection. In this paper, we investigate two parallel methods of accelerating w-projection processing on multiple nodes: the hybrid Message Passing Interface (MPI) and Open Multi-Processing (OpenMP) method based on multicore Central Processing Units (CPUs) and the hybrid MPI and Compute Unified Device Architecture (CUDA) method based on Graphics Processing Units (GPUs). Both methods are successfully employed and operated in various computational environments, confirming their robustness. The experimental results show that the total runtime of both MPI + OpenMP and MPI + CUDA methods is significantly shorter than that of single-thread processing. MPI + CUDA generally shows faster performance when running on multiple nodes than MPI + OpenMP, especially on large numbers of nodes. The single-precision GPU-based processing yields faster computation than the double-precision processing; while the single- and doubleprecision CPU-based processing shows consistent computational performance. The gridding time remarkably increases when the support size of the convolution kernel is larger than 8 and the image size is larger than 2,048 pixels. The present research offers useful guidance for developing SKA imaging pipelines., 9 pages. Published in Science Bulletin, 64, 586-594 (2019)

In this paper, we present a deep learning-based recognition algorithm to identify pulsars by observing data containing millions of candidates including radio frequency interference and noise sources. The dataset is obtained from the High Time Resolution Universe survey created and updated by the Parkes telescope. We investigate several effective single and combined features via simple logistic regression. To deal with the imbalanced dataset, we oversimplify the original dataset at different sampling rates, which is also one of the learning parameters. After training the pre-processed dataset via a convolutional neural network, we provide a cross-validated evaluation of all candidates. Results show that the deep-learning based recognition algorithm can identify the pulsar and radio frequency interference signals with high accuracy. The precision and recall of radio frequency interference are both 100%, and those of pulsars are 91% and 94%, respectively.

This work presents a workflow for simulating and processing the full-scale low-frequency telescope data of the Square Kilometre Array (SKA) Phase 1. The SKA project will enter the construction phase soon, and once completed, it will be the world’s largest radio telescope and one of the world’s largest data generators. The authors used Summit to mimic an endto-end SKA workflow, simulating a dataset of a typical 6 hour observation and then processing that dataset with an imaging pipeline. This workflow was deployed and run on 4,560 compute nodes, and used 27,360 GPUs to generate 2.6 PB of data. This was the first time that radio astronomical data were processed at this scale. Results show that the workflow has the capability to process one of the key SKA science cases, an Epoch of Reionization observation. This analysis also helps reveal critical design factors for the next-generation radio telescopes and the required dedicated processing facilities.

We present the observational results from the 43-GHz Very Long Baseline Array (VLBA) observations of 124 compact radio-loud active galactic nuclei (AGNs) that were conducted between 2014 November and 2016 May. The typical dimensions of the restoring beam in each image are about 0.5 mas $\times$ 0.2 mas. The highest resolution of 0.2 mas corresponds to a physical size of 0.02 pc for the lowest redshift source in the sample. The 43-GHz very long baseline interferometry (VLBI) images of 97 AGNs are presented for the first time. We study the source compactness on milli-arcsec (mas) and sub-mas scales, and suggest that 95 sources in our sample are suitable for future space VLBI observations. By analyzing our data supplemented with other VLBA AGN surveys from literature, we find that the core brightness temperature increases with increasing frequency below a break frequency ~ 7 GHz, and decreases between ~7--240~GHz but increases again above~240 GHz in the rest frame of the sources. This indicates that the synchrotron opacity changes from optically thick to thin. We also find a strong statistical correlation between radio and $\gamma$-ray flux densities. Our correlation is tighter than those in literature derived from lower-frequency VLBI data, suggesting that the $\gamma$-ray emission is produced more co-spatially with the 43-GHz VLBA core emission. This correlation can also be extrapolated to the un-beamed AGN population, implying that a universal $\gamma$-ray production mechanism might be at work for all types of AGNs., Comment: Accepted for publication in ApJS

The Data Activated Liu 1 Graph Engine – DALiuGE 2 – is an execution framework for processing large astronomical datasets at a scale required by the Square Kilometre Array Phase 1 (SKA1). It includes an interface for expressing complex data reduction pipelines consisting of both datasets and algorithmic components and an implementation run-time to execute such pipelines on distributed resources. By mapping the logical view of a pipeline to its physical realisation, DALiuGE separates the concerns of multiple stakeholders, allowing them to collectively optimise large-scale data processing solutions in a coherent manner. The execution in DALiuGE is data-activated, where each individual data item autonomously triggers the processing on itself. Such decentralisation also makes the execution framework very scalable and flexible, supporting pipeline sizes ranging from less than ten tasks running on a laptop to tens of millions of concurrent tasks on the second fastest supercomputer in the world. DALiuGE has been used in production for reducing interferometry datasets from the Karl E. Jansky Very Large Array and the Mingantu Ultrawide Spectral Radioheliograph; and is being developed as the execution framework prototype for the Science Data Processor (SDP) consortium of the Square Kilometre Array (SKA) telescope. This paper presents a technical overview of DALiuGE and discusses case studies from the CHILES and MUSER projects that use DALiuGE to execute production pipelines. In a companion paper, we provide in-depth analysis of DALiuGE’s scalability to very large numbers of tasks on two supercomputing facilities.

Space very long baseline interferometry (VLBI) has unique applications in high-resolution imaging of fine structure of astronomical objects and high-precision astrometry due to the key long space-Earth or space-space baselines beyond the Earth's diameter. China has been actively involved in the development of space VLBI in recent years. This review briefly summarizes China's research progress in space VLBI and the future development plan., published in Advances in Space Research, special issue (Space VLBI)

New pulsar searching methods can significantly enhance the discovery of new pulsars and observations of single pulsars. This paper searches for pulsars among the observations centered at 185 MHz collected by the Murchison Wide-field Array (MWA). The declinations of two high-latitude northern sky pulsars, identified as J0332+5434 and J2219+4754, exceeded the commonly considered detection limit of MWA (declination≤30°), and appeared in the side lobes of MWA’s incoherent beams. Although the sensitivity is substantially lower in the side lobes than in the main lobe, pulsars are generally very bright at low frequencies, so are observable in the side lobes of low-frequency, wide field of view telescopes. This article describes the search for pulsars with the MWA, and discusses the possibility of further searching and observing the northern sky pulsars in the MWA side lobes. In a simulation study, the MWA detection sensitivity was evaluated at several flux densities of known radio pulsars. We estimated that approximately 30 known northern sky pulsars can be detected in the MWA side lobes. This work promises future pulsar searching by the Square Kilometer Array at low frequencies.

Radio-loud high-redshift quasars (HRQs), although only a few of them are known to date, are crucial for the studies of the growth of supermassive black holes (SMBHs) and the evolution of active galactic nuclei (AGN) at early cosmological epochs. Radio jets offer direct evidence of SMBHs, and their radio structures can be studied with the highest angular resolution using Very Long Baseline Interferometry (VLBI). Here we report on the observations of three HRQs (J0131-0321, J0906+6930, J1026+2542) at z>5 using the Korean VLBI Network and VLBI Exploration of Radio Astrometry Arrays (together known as KaVA) with the purpose of studying their pc-scale jet properties. The observations were carried out at 22 and 43 GHz in 2016 January among the first-batch open-use experiments of KaVA. The quasar J0906+6930 was detected at 22 GHz but not at 43 GHz. The other two sources were not detected and upper limits to their compact radio emission are given. Archival VLBI imaging data and single-dish 15-GHz monitoring light curve of J0906+6930 were also acquired as complementary information. J0906+6930 shows a moderate-level variability at 15 GHz. The radio image is characterized by a core-jet structure with a total detectable size of ~5 pc in projection. The brightness temperature, 1.9x10^{11} K, indicates relativistic beaming of the jet. The radio properties of J0906+6930 are consistent with a blazar. Follow-up VLBI observations will be helpful for determining its structural variation., accepted for publication in Monthly Notices of the Royal Astronomical Society Main Journal. 8 pages, 3 figures