Square Kilometre Array Research Articles

YOLO-CIANNA: Galaxy detection with deep learning in radio data

Context. The upcoming Square Kilometer Array (SKA) will set a new standard regarding data volume generated by an astronomical instrument, which is likely to challenge widely adopted data-analysis tools that scale inadequately with the data size. Aims. The aim of this study is to develop a new source detection and characterization method for massive radio astronomical datasets based on modern deep-learning object detection techniques. For this, we seek to identify the specific strengths and weaknesses of this type of approach when applied to astronomical data. Methods. We introduce YOLO-CIANNA, a highly customized deep-learning object detector designed specifically for astronomical datasets. In this paper, we present the method and describe all the elements introduced to address the specific challenges of radio astronomical images. We then demonstrate the capabilities of this method by applying it to simulated 2D continuum images from the SKA observatory Science Data Challenge 1 (SDC1) dataset. Results. Using the SDC1 metric, we improve the challenge-winning score by +139% and the score of the only other post-challenge participation by +61%. Our catalog has a detection purity of 94% while detecting 40–60% more sources than previous top-score results, and exhibits strong characterization accuracy. The trained model can also be forced to reach 99% purity in post-process and still detect 10–30% more sources than the other top-score methods. It is also computationally efficient, with a peak prediction speed of 500 images of 512×512 pixels per second on a single GPU. Conclusions. YOLO-CIANNA achieves state-of-the-art detection and characterization results on the simulated SDC1 dataset and is expected to transfer well to observational data from SKA precursors.

Reionization Parameter Inference from 3D Minkowski Functionals of the 21 cm Signals

The Minkowski functionals (MFs), a set of topological summary statistics, have emerged as a powerful tool for extracting non-Gaussian information. We investigate the prospect of constraining the reionization parameters using the MFs of the 21 cm brightness temperature field from the epoch of reionization (EOR). Realistic effects, including thermal noise, synthesized beam, and foreground avoidance, are applied to the mock observations from radio interferometric array experiments such as the Hydrogen Epoch of Reionization Array (HERA) and the Square Kilometre Array (SKA). We demonstrate that the MFs of the 21 cm signal measured with SKA-Low can be used to distinguish different reionization models, whereas the MF measurement with a HERA-like array cannot be made accurately enough. We further forecast the accuracies with which the MF measurements can place constraints on reionization parameters, using the standard Markov Chain Monte Carlo analysis for parameter inference based on forward modeling. We find that for SKA-Low observation, MFs provide unbiased estimations of the reionization parameters with accuracies comparable to the power spectrum (PS) analysis. Furthermore, joint constraints using both MFs and PS can improve the constraint accuracies by up to 30% compared to those with the PS alone. Nevertheless, the constraint accuracies can be degraded if the EOR window is shrunk with strong foreground avoidance. Our analysis demonstrates the promise of MFs as a set of summary statistics that extract complementary information from the 21 cm EOR field to the two-point statistics, which suggests a strong motivation for incorporating the MFs into the data analysis of future 21 cm observations.

Self-supervised learning on MeerKAT wide-field continuum images

Context. Self-supervised learning (SSL) applied to natural images has demonstrated a remarkable ability to learn meaningful, low-dimension representations without labels, resulting in models that are adaptable to many different tasks. Until now, applications of SSL to astronomical images have been limited to Galaxy Zoo datasets, which require a significant amount of preprocessing to prepare sparse images centered on a single galaxy. With wide-field survey instruments at the forefront of the Square Kilometer Array (SKA) era, this approach to gathering training data is impractical. Aims. We demonstrate that continuum images from surveys such as the MeerKAT Galactic Cluster Legacy Survey (MGCLS) can be successfully used with SSL, without extracting single-galaxy cutouts. Methods. Using the SSL framework DINO, we experimented with various preprocessing steps, augmentations, and architectures to determine the optimal approach for this data. We trained both ResNet50 and Vision Transformer (ViT) backbones. Results. Our models match state-of-the-art results (trained on Radio Galaxy Zoo) for FRI/FRII morphology classification. Furthermore, they predict the number of compact sources via linear regression with much higher accuracy. Open-source foundation models trained on natural images such as DINOv2 also excel at simple FRI/FRII classification; the advantage of domain-specific backbones is much smaller models trained on far less data. Smaller models are more efficient to fine-tune, and doing so results in a similar performance between our models, the state-of-the-art, and open-source models on multi-class morphology classification. Conclusions. Using source-rich crops from wide-field images to train multi-purpose models is an easily scalable approach that significantly reduces data preparation time. For the tasks evaluated in this work, twenty thousand crops is sufficient training data for models that produce results similar to state-of-the-art. In the future, complex tasks like source detection and characterization, together with domain-specific tasks, ought to demonstrate the true advantages of training models with radio astronomy data over natural-image foundation models.

An evaluation of source-blending impact on the calibration of SKA EoR experiments

ABSTRACT Twenty-one-centimetre signals from the Epoch of Reionization (EoR) are expected to be detected in the low-frequency radio window by the next-generation interferometers, particularly the Square Kilometre Array (SKA). However, precision data analysis pipelines are required to minimize the systematics within an infinitesimal error budget. Consequently, there is a growing need to characterize the sources of errors in EoR analysis. In this study, we identify one such error origin, namely source blending, which is introduced by the overlap of objects in the densely populated observing sky under SKA1-Low’s unprecedented sensitivity and resolution, and evaluate its two-fold impact in both the spatial and frequency domains using a novel hybrid evaluation (HEVAL) pipeline combining end-to-end simulation with an analytic method to mimic EoR analysis pipelines. Sky models corrupted by source blending induce small but severe frequency-dependent calibration errors when coupled with astronomical foregrounds, impeding EoR parameter inference with strong additive residuals in the two-dimensional power spectrum space. We report that additive residuals from poor calibration against sky models with blending ratios of 5 and 0.5 per cent significantly contaminate the EoR window. In contrast, the sky model with a 0.05 per cent blending ratio leaves little residual imprint within the EoR window, therefore identifying a blending tolerance at approximately 0.05 per cent. Given that the SKA observing sky is estimated to suffer from an extended level of blending, strategies involving de-blending, frequency-dependent error mitigation, or a combination of both, are required to effectively attenuate the calibration impact of source-blending defects.

Using 26Al to detect ongoing self-enrichment in young massive star clusters

Abstract Self-enrichment is one of the leading explanations for chemical anomalies in globular clusters. In this scenario, various candidate polluter stars have been proposed to eject gas with altered chemical composition during the self-enrichment process. Most of the proposed polluters will also eject radioactive 26Al into the surroundings. Hence, any detection of 26Al in young massive star clusters (YMCs) would support the self-enrichment scenario if YMCs were indeed the progenitors of globular clusters. Observations of gamma-ray data from COMPTEL and INTEGRAL, as well as detections of 26AlF molecules by the Atacama Large Millimeter-submillimeter Array (ALMA), indicate the maturing of 26Al detection methods. Detection possibilities will be enhanced in the short- to mid-term by the upcoming launch of the Compton Spectrometer and Imager (COSI). The Square Kilometer Array (SKA) could in principle also detect radio recombination lines of the positronium formed from the decay products of 26Al. Here, we show for a sample of YMCs in the nearby Universe, where self-enrichment could plausibly take place. For some nearby galaxies, this could enhance 26Al by an order of one magnitude. Detecting 26AlF with ALMA appears feasible for many candidate self-enrichment clusters, although significant challenges remain with other detection methods. The Large Magellanic Cloud, with its YMC R136, stands out as the most promising candidate. Detecting a 1.8 MeV radioactive decay line of 26Al here would require at least 15 months of targeted observation with COSI, assuming ongoing self-enrichment in R136.

A Real-Time Adaptive Station Beamforming Strategy for Next Generation Phased Array Radio Telescopes.

The next generation phased array radio telescopes, such as the Square Kilometre Array (SKA) low frequency aperture array, suffer from RF interference (RFI) because of the large field of view of antenna element. The classical station beamformer used in SKA-low is resource efficient but cannot deal with the unknown sidelobe RFI. A real-time adaptive beamforming strategy is proposed for SKA-low station, which trades the capability of adaptive RFI nulling at an acceptably cost, it doesn't require hardware redesign but only modifies the firmware accordingly. The proposed strategy uses a Parallel Least Mean Square (PLMS) algorithm, which has a computational complexity of 4N+2 and can be performed in parallel. Beam pattern and output SINR simulation results show deeply nulling performance to sidelobe RFI, as well as good mainlobe response similar to the classical beamformer. The convergence performance depends on the signal-and-interference environments and step size, wherein too large a step size leads to a non-optimal output SINR and too small a step size leads to slow convergence speed. FPGA implementation demonstrations are implemented and tested on a NI FPGA module, and test results demonstrate good real-time performance and low slice resource consumption.

PyC[formula omitted]Ray: A flexible and GPU-accelerated radiative transfer framework for simulating the cosmic epoch of reionization

Detailed modeling of the evolution of neutral hydrogen in the intergalactic medium during the Epoch of Reionization, 5≤z≤20, is critical in interpreting the cosmological signals from current and upcoming 21-cm experiments such as the Low-Frequency Array (LOFAR) and the Square Kilometre Array (SKA). Numerical radiative transfer codes provide the most physically accurate models of the reionization process. However, they are computationally expensive as they must encompass enormous cosmological volumes while accurately capturing astrophysical processes occurring at small scales (≲Mpc). Here, we present pyC2Ray, an updated version of the massively parallel ray-tracing and chemistry code, C2-Ray, which has been extensively employed in reionization simulations. The most time-consuming part of the code is calculating the hydrogen column density along the path of the ionizing photons. Here, we present the Accelerated Short-characteristics Octahedral ray-tracing (ASORA) method, a ray-tracing algorithm specifically designed to run on graphical processing units (GPUs). We include a modern Python interface, allowing easy and customized use of the code without compromising computational efficiency. We test pyC2Ray on a series of standard ray-tracing tests and a complete cosmological simulation with volume size (349Mpc)3, mesh size of 2503 and approximately 106 sources. Compared to the original code, pyC2Ray achieves the same results with negligible fractional differences, ∼10−5, and a speedup factor of two orders of magnitude. Benchmark analysis shows that ASORA takes a few nanoseconds per source per voxel and scales linearly for an increasing number of sources and voxels within the ray-tracing radii.

How informative are summaries of the cosmic 21 cm signal?

The cosmic 21 cm signal will bring data-driven advances to studies of Cosmic Dawn (CD) and the Epoch of Reionization (EoR). Radio telescopes such as the Square Kilometre Array (SKA) will eventually map the HI fluctuations over the first billion years -- the majority of our observable Universe. With such large data volumes, it becomes increasingly important to develop optimal summary statistics, which will allow us to learn as much as possible about the CD and EoR. In this work we compare the astrophysical parameter constraining power of several 21 cm summary statistics, using the determinant of the Fisher information matrix, $ F $. Since we do not have an established fiducial model for the astrophysics of the first galaxies, we computed for each summary the distribution of $ F $ across the prior volume. Using a large database of cosmic 21 cm light cones that include realizations of telescope noise, we compared the following summaries: (i) the spherically averaged power spectrum (1DPS), (ii) the cylindrically averaged power spectrum (2DPS), (iii) the 2D wavelet scattering transform (WST), (iv) a recurrent neural network (RNN) trained as a regressor; (v) an information-maximizing neural network (IMNN); and (vi) the combination of 2DPS and IMNN. Our best performing individual summary is the 2DPS, which provides relatively high Fisher information throughout the parameter space. Although capable of achieving the highest Fisher information for some parameter choices, the IMNN does not generalize well, resulting in a broad distribution across the prior volume. Our best results are achieved with the concatenation of the 2DPS and IMNN. The combination of only these two complimentary summaries reduces the recovered parameter variances on average by factors of sim 6.5 -- 9.5, compared with using each summary independently. Finally, we point out that that the common assumption of a constant covariance matrix when doing Fisher forecasts using 21 cm summaries can significantly underestimate parameter constraints.

Wavelet-based statistics for enhanced 21cm EoR parameter constraints

We propose a new approach to improve the precision of astrophysical parameter constraints for the 21cm signal from the epoch of reionisation (EoR). Our method introduces new sets of summary statistics, hereafter ‘evolution compressed’ statistics, which quantify the spectral evolution of the 2D spatial statistics computed at fixed redshift. We defined such compressed statistics for power spectrum (PS), wavelet scattering transforms (WST), and wavelet moments (WM), which also characterise non-Gaussian features. To compare these different statistics, along with the 3D power spectrum, we estimated their Fisher information on three cosmological parameters from an ensemble of simulations of 21cm EoR data, both in noiseless and noisy scenarios using Square Kilometre Array (SKA) noise levels equivalent to 100 and 1000 h of observations. We also compare wavelet statistics, in particular WST, built from standard directional Morlet wavelets, as well as from a set of isotropic wavelets derived from the binning window function of the 2D power spectrum. For the noiseless case, the compressed wavelet statistics give constraints that are up to five times more precise than those obtained from the 3D isotropic power spectrum. At the same time, for 100 h SKA noise, from which it is difficult to extract non-Gaussian features, compressed wavelet statistics still give over 30% tighter constraints. We find that the wavelet statistics with wavelets derived from the power-spectrum binning window function provide the tightest constraints of all the statistics, with the WSTs seemingly performing better than the WMs, in particular when working with noisy data. The findings of this study demonstrate that evolution-compressed statistics extract more information than usual 3D isotropic power-spectra approaches and that our wavelet-based statistics can consistently outmatch power-spectrum-based statistics. When constructing such wavelet-based statistics, we also emphasise the need to choose a set of wavelets with an appropriate spectral resolution concerning the astrophysical process studied.

H i galaxy signatures in the SARAO MeerKAT galactic plane survey − III. Unveiling the obscured part of the Vela Supercluster

ABSTRACT We conducted a search for $\textrm {H}\, \scriptstyle \mathrm{I}$ emission of the gas-rich galaxies in the Vela region (260° ≤ ℓ ≤ 290°, −2° ≤ b ≤ 1°) to explore the Vela Supercluster (VSCL) at Vhel ∼ 18 000 $\rm km\, s^{-1}$, largely obscured by Galactic dust. Within the mostly Radio Frequency Interference-free band (250 < Vhel < 25 000 $\rm km\, s^{-1}$) of MeerKAT, the analysis focuses on 157 hexagonally distributed pointings extracted from the South African Radio Astronomy Observatory MeerKAT Galactic Plane Survey located in the Vela region (Vela−SMGPS). These were combined into 10 contiguous mosaics, covering an ∼90 square degrees area. Among the 843 $\textrm {H}\, \scriptstyle \mathrm{I}$ detected sources, 39 were previously discovered in the H i Parkes Zone of Avoidance survey (Vhel < 12 000 $\rm km\, s^{-1}$; rms ∼ 6 $\rm mJy\, beam^{-1}$). With the improved rms level of the Vela−SMGPS, i.e. 0.29–0.56 $\rm mJy\, beam^{-1}$, our study unveils nearly 12 times more detections (471 candidates) in that same velocity range. We furthermore could identify 187 galaxy candidates with an $\textrm {H}\, \scriptstyle \mathrm{I}$ mass limit reaching $\log (M_{\rm HI}/\rm {\rm M}_{\odot }) = 9.44$ in the VSCL velocity range Vhel ∼ 19 500 ± 3500 $\rm km\, s^{-1}$. We find indications of two wall-like overdensities that confirm the original suspicion that these walls intersect at low latitudes around longitudes of ℓ ∼ 272°–278°. We also find a strong signature most likely associated with the Hydra/Antlia extension and evidence of a previously unknown narrow filament at Vhel ∼ 12 000 $\rm km\, s^{-1}$. This paper demonstrates the efficiency of systematic $\textrm {H}\, \scriptstyle \mathrm{I}$ surveys with the Square Kilometre Array (SKA) precursor MeerKAT, even in the most obscured part of the Zone of Avoidance.

A versatile framework for analyzing galaxy image data by incorporating Human-in-the-loop in a large vision model* *The authors acknowledge the support from the National Natural Science Foundation of China (Grant Nos. 12173027, 12303105, 12173062), the National Key R & D Program of China (Grant Nos. 2023YFF0725300, 2022YFF0503402), the Science Research Grants from the Square Kilometre Array (SKA) (2020SKA0110100), the Science

The exponential growth of astronomical datasets provides an unprecedented opportunity for humans to gain insight into the Universe. However, effectively analyzing this vast amount of data poses a significant challenge. In response, astronomers are turning to deep learning techniques, but these methods are limited by their specific training sets, leading to considerable duplicate workloads. To overcome this issue, we built a framework for the general analysis of galaxy images based on a large vision model (LVM) plus downstream tasks (DST), including galaxy morphological classification, image restoration, object detection, parameter extraction, and more. Considering the low signal-to-noise ratios of galaxy images and the imbalanced distribution of galaxy categories, we designed our LVM to incorporate a Human-in-the-loop (HITL) module, which leverages human knowledge to enhance the reliability and interpretability of processing galaxy images interactively. The proposed framework exhibits notable few-shot learning capabilities and versatile adaptability for all the abovementioned tasks on galaxy images in the DESI Legacy Imaging Surveys. In particular, for the object detection task, which was trained using 1000 data points, our DST in the LVM achieved an accuracy of 96.7%, while ResNet50 plus Mask R-CNN reached an accuracy of 93.1%. For morphological classification, to obtain an area under the curve (AUC) of ~0.9, LVM plus DST and HITL only requested 1/50 of the training sets that ResNet18 requested. In addition, multimodal data can be integrated, which creates possibilities for conducting joint analyses with datasets spanning diverse domains in the era of multi-messenger astronomy.

An Empirical Study On Imaging Sensitivity Of SKA1-LOW Based on Point Source Sensitivity

The Square Kilometre Array (SKA) radio telescope will achieve unparalleled sensitivity and angular resolution, substantially progressing our research into the formation and development of the early universe. Sensitivity is one of the most critical telescope metrics, often used to determine the observing mode and observing time of a radio source. It is also used to determine imaging parameters to balance imaging sensitivity and spatial resolution during data processing. In this paper, to meet the needs of continuum imaging pipeline data processing for the SKA1-LOW telescope, we derive equations for point-source-sensitivity (PSS) calculations under multichannel, multipolarization, discrete frequency sampling, discrete time sampling, and different weighting schemes. After validating the correctness of the equation, we improved and refined the sensitivity calculation application in the Radio Astronomy Simulation, Calibration, and Imaging Library, and further calculated the variation of the PSS versus angular resolution for the full-scale SKA1-LOW with different weighting schemes, and provided SKA1-LOW broadband image performance as functions of angular scale for four frequencies. These results can provide not only a theoretical basis for the construction and commissioning of the SKA1-LOW telescope but also guidance for imaging processing in future scientific data processing.

Radio Emission from High-redshift Active Galactic Nuclei in the JADES and CEERS Surveys

Recent calculations indicate that radio emission from quasars at z ∼ 6–7 could be detected at much earlier stages of evolution, at z ∼ 14–15, by the Next-Generation Very Large Array (ngVLA) and the Square Kilometer Array (SKA). However, the James Webb Space Telescope has now discovered less luminous active galactic nuclei (AGNs) at z > 4 and a few massive black holes (BHs) at z > 10, which may be the progenitors of supermassive black holes (SMBHs) but at different stages of growth. Radio detections of these new AGNs would provide complementary measures of their properties and those of their host galaxies. Here we estimate radio flux densities for 19 new AGNs found by the JADES, CEERS, and UNCOVER surveys. We find that ngVLA should be able to detect most of these sources in targeted surveys with integration times of 10–100 hr (and in just 1 hr for a few of them) but most would require at least 100 hr of SKA time in spite of its greater sensitivities at low frequencies. In some cases, radio emission from the BH can be distinguished from that of H ii regions and supernovae in their host galaxies, which could be used to estimate their star formation rates. Such detections would be yet another example of the useful synergies between near-infrared and radio telescopes in SMBH science in the coming decade.

Inverse Gertsenshtein effect as a probe of high-frequency gravitational waves

We apply the inverse Gertsenshtein effect, i.e., the graviton-photon conversion in the presence of a magnetic field, to constrain high-frequency gravitational waves (HFGWs).Using existing astrophysical measurements, we compute upper limits on the GW energy densities ΩGW at 16 different frequency bands.Given the observed magnetisation of galaxy clusters with field strength B ∼ μG correlated on 𝒪(10) kpc scales, we estimate HFGW constraints in the 𝒪(102) GHz regime to be ΩGW ≲ 1016 with the temperature measurements of the Atacama Cosmology Telescope (ACT).Similarly, we conservatively obtain ΩGW ≲ 1013 (1011) in the 𝒪(102) MHz (𝒪(10) GHz) regime by assuming uniform magnetic field with strength B ∼ 0.1 nG and saturating the excess signal over the Cosmic Microwave Background (CMB) reported by radio telescopes such as the Experiment to Detect the Global EoR Signature (EDGES), LOw Frequency ARray (LOFAR), and Murchison Widefield Array (MWA), and the balloon-borne second generation Absolute Radiometer for Cosmology, Astrophysics, and Diffuse Emission (ARCADE2)with graviton-induced photons. The upcoming Square Kilometer Array (SKA) can tighten these constraints by roughly 10 orders of magnitude, which will be a step closer to reaching the critical value of ΩGW = 1 or the Big Bang Nucleosynthesis (BBN) bound of ΩGW ≃ 1.2 × 10-6.We point to future improvement of the SKA forecast and estimate that proposed CMB measurement at the level of 𝒪(100-2) nK, such as Primordial Inflation Explorer (PIXIE) and Voyage 2050, are needed to viably detect stochastic backgrounds of HFGWs.

SKA sensitivity to sub-GeV dark matter decay: Synchrotron radio emissions in white dwarf magnetospheres

We investigate the potential of the Square Kilometre Array (SKA) in detecting synchrotron radiation emitted from the decay of sub-GeV dark matter (dark matter with masses below the GeV scale) in the presence of strong magnetic fields. As a concrete setup, we consider scenarios where the magnetosphere of a magnetic white dwarf overlaps with dense dark matter environments, such as those surrounding a primordial black hole. Our study reveals that the encounters of compact objects such as white dwarfs and black holes offer a promising avenue for upcoming radio telescopes to probe the properties of light dark matter, which has been less explored compared with more conventional heavier (masses above the GeV scale) dark matter. Published by the American Physical Society 2024

Observability of Substructures in the Planet-forming Disk in the (Sub)centimeter Wavelength with SKA and ngVLA

Current imaging observations of protoplanetary disks using the Atacama Large Millimeter/submillimeter Array (ALMA) primarily focus on the submillimeter wavelength, leaving a gap in effective observational approaches for centimeter-sized dust, which is crucial to the issue of planet formation. The forthcoming Square Kilometre Array (SKA) and ngVLA may rectify this deficiency. In this paper, we employ multifluid hydrodynamic numerical simulations and radiative transfer calculations to investigate the potential of SKA1-Mid, ngVLA, and SKA2 for imaging protoplanetary disks at subcentimeter/centimeter wavelengths. We create mock images with ALMA/SKA/ngVLA at multiwavelengths based on the hydrodynamical simulation output and test different sensitivity and spatial resolutions. We discover that both SKA and ngVLA will serve as excellent supplements to the existing observational range of ALMA, and their high resolution enables them to image substructures in the disk’s inner region (∼5 au from the stellar). Our results indicate that SKA and ngVLA can be utilized for more extended monitoring programs in the centimeter wave band. While in the subcentimeter range, ngVLA possesses the capability to produce high-fidelity images within shorter observation times (∼1 hr on source time) than previous research, holding potential for future survey observations. We also discuss for the first time the potential of SKA2 for observing protoplanetary disks at a 0.7 cm wavelength.

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.

A Uniformly Selected Sample of Low-mass Black Holes in Seyfert 1 Galaxies. III. Radio Sources from the SKA Pathfinders and Beyond

Occupying the intermediate-mass regime of the accretion-jet parameter space, radio continuum emission from active galactic nuclei (AGNs) with black hole mass M BH ≲ 106 M ⊙ (low-mass AGNs) is a valuable probe to the physics of relativistic jets. Yet the number of low-mass AGNs with radio detection is rather limited so far (≈40 in total). In this work, we make two efforts to search for radio counterparts for the largest sample of optically selected low-mass AGNs. First, we collect counterparts from the recent data releases of Square Kilometre Array (SKA) pathfinders such as LOFAR Two-meter Sky Survey (LoTSS). Additionally, we deeply mine in Faint Images of the Radio Sky at Twenty-Centimeters (FIRST), fitting the FIRST images of the optical AGNs with an elaborate procedure optimized to detect faint radio sources. We have obtained 151 radio sources (mainly from the SKA pathfinders), including 102 new reliable sources (signal-to-noise ratio, hereafter S/N, ≥ 5) and 23 new candidates (3.5 ≤ S/N < 5). The majority of these new sources (119 of 125) have flux densities lower than the threshold of the official FIRST catalog. The new sources have rest-frame 20 cm power (P 20 cm) from 1.98 × 1020 to 1.29 × 1023 W Hz−1. For low-z Seyfert galaxies, P 20 cm correlates with M BH intrinsically and positively, yet only marginally with Eddington ratio L/L Edd. In terms of the log N–log S relation for the expanding Universe, the limiting flux density for the completeness of our LoTSS sources turns out to be 0.45 mJy at 1.4 GHz; i.e., complete to such a flux-density level that is 4 times deeper than the official FIRST catalog.

Machine-learning recovery of foreground wedge-removed 21-cm light cones for high-z galaxy mapping

ABSTRACT Upcoming experiments will map the spatial distribution of the 21-cm signal over three-dimensional volumes of space during the Epoch of Reionization (EoR). Several methods have been proposed to mitigate the issue of astrophysical foreground contamination in tomographic images of the 21-cm signal, one of which involves the excision of a wedge-shaped region in cylindrical Fourier space. While this removes the k-modes most readily contaminated by foregrounds, the concurrent removal of cosmological information located within the wedge considerably distorts the structure of 21-cm images. In this study, we build upon a U-Net-based deep-learning algorithm to reconstruct foreground wedge-removed maps of the 21-cm signal, newly incorporating light-cone effects. Adopting the Square Kilometre Array as our fiducial instrument, we highlight that our U-Net recovery framework retains a reasonable level of reliability even in the face of instrumental limitations and noise. We subsequently evaluate the efficacy of recovered maps in guiding high-redshift galaxy searches and providing context to existing galaxy catalogues. This will allow for studies of how the high-redshift galaxy luminosity function varies across environments, and ultimately refine our understanding of the connection between the ionization state of the intergalactic medium and galaxies during the EoR.

Systematic Radio Telescope Alignment Using Portable Fringe Projection Profilometry

In 2019, the Event Horizon Telescope (EHT) released the first-ever image of a black hole event horizon. Astronomers are now aiming for higher angular resolutions of distant targets, like black holes, to understand more about the fundamental laws of gravity that govern our universe. To achieve this higher resolution and increased sensitivity, larger radio telescopes are needed to operate at higher frequencies and in larger quantities. Projects like the next-generation Very Large Array (ngVLA) and the Square-Kilometer Array (SKA) require building hundreds of telescopes with diameters greater than 10 ms over the next decade. This has a twofold effect. Radio telescope surfaces need to be more accurate to operate at higher frequencies, and the logistics involved in maintaining a radio telescope need to be simplified to support them properly in large quantities. Both of these problems can be solved with improved methods for surface metrology that are faster and more accurate with a higher resolution. This leads to faster and more accurate panel alignment and, therefore, a more productive observatory. In this paper, we present the use of binocular fringe projection profilometry as a solution to this problem and demonstrate it by aligning two panels on a 3-m radio telescope dish. The measurement takes only 10 min and directly delivers feedback on the tip, tilt, and piston of each panel to create the ideal reflector shape.