Radio Astronomy Research Articles (Page 1)

In the first paper of this series, we presented radio observations of three giant double-double radio galaxies: J1021+1216, J1528+0544, and J2345--0449. We reported the asymmetries and minor misalignments identified in the outer and inner doubles of all three sources, in addition to an uncommon trace of emission with a relatively flat spectrum in the spectral index map of J1528+0544. Furthermore, we discovered core extensions in the J1021+1216 and J1528+0544 high-resolution maps, suggesting that the two sources are triple-double radio galaxies. In this paper, we continue our investigation of the three sources in search of the causes behind these observed peculiarities. Our goal is to carry out a detailed study of a selection sample of giant double-double radio galaxies. By determining the properties of these sources and their environments, we obtained a comprehensive image of the processes influencing their evolution, which we could then use to make comparisons with the model results on radio-galaxy evolution from the literature. In this work, we used the radio maps prepared and presented in the first paper of this study to perform a spectral aging analysis with the Broadband Radio Astronomy ToolS software and dynamical modeling with the dynage software. From this modeling, we recovered a range of parameters describing the conditions in and around the observed sources, including the duration of the active and quiescent phases, jet power, and external medium density. Based on our radiative and dynamical models, we report long durations for the active phases in the outer doubles of J1021+1216 and J2345--0449. We report ages of t_ rad,J10 =43±4 Myr and t_ dyn,J10 =250 Myr for J1021+1216, and t_ rad,J23 =42±4 Myr and t_ dyn,J23 =176 Myr for J2345--0449. The inner double of J1021+1216 was found to be expanding at a speed ∼!!0.5c inside a relic cocoon with a density of łog(̊ho_0 : kg: m^ )=-25.7. In J1528+0544, all the parameters that could influence the evolution of the outer lobes are not out of the ordinary. However, we found a radiatively young structure in the outer lobes, which we interpreted as a trace of a restarted jet belonging to an ``intermediate'' phase of activity. We conclude that there is no single universal factor stimulating the growth of the GRGs. In J1021+1216 and J2345--0449 outer doubles, with projected sizes ∼1.85 Mpc and ∼1.7 Mpc, respectively, the main factor stimulating their growth is the exceptionally long duration of their active phases. In J1021+1216 inner double, with a projected size of ∼1 Mpc, the main factor is its fast expansion inside a low-density medium. The outer double J1528+0544, with a projected size ∼715 kpc, represents the case of a giant radio galaxy, where growth was stimulated by the recurrent activity of the galactic nucleus. Furthermore, we report the discovery of two radio galaxies with three separate phases of activity visible at once: J1021+1216 and J1528+0544.

The Irbene single-baseline radio interferometer (ISBI), operated by the Ventspils International Radio Astronomy Centre (VIRAC), offers a rare and versatile configuration in modern radio astronomy. Combining the 32-m and 16-m fully steerable parabolic radio telescopes separated by an 800-m baseline, this system possesses a unique capability for high-sensitivity, time-domain interferometric observations. Unlike large interferometric arrays optimized for sub-arcsecond resolution imaging, the Irbene system is tailored for studies that require high temporal resolution and a strong signal-to-noise ratio. This paper reviews key scientific applications of the Irbene interferometer, including simultaneous methanol maser and radio continuum variability studies, high-cadence monitoring of quasi-periodic pulsations (QPPs) in stellar flares, ionospheric diagnostics using GNSS signals, orbit determination of navigation satellites and forward scatter radar techniques for space object detection. These diverse applications demonstrate the scientific potential of compact interferometric systems in an era dominated by large-scale observatories.

Context. Radio interferometers provide the means to perform the wide field-of-view (FoV) high-sensitivity observations required for modern radio surveys. As computing power per cost has increased, there has been a move toward larger arrays of smaller dishes, such as DSA-2000, the upcoming HIRAX, CHORD, and SKA radio telescopes. Such arrays can have simpler receiver designs with room-temperature low-noise amplifiers and use direct sampling to greatly reduce the cost per antenna. The ARGOS project is currently developing an array of five six-meter antennas that will be used to demonstrate the technology required for a next-generation “small-D, big-N” radio interferometer in Europe. Aims. For this work our objective was to implement a first-stage digital signal processing system for the ARGOS demonstrator array, providing digitization, channelization, delay correction, and frequency-dependent complex gain correction. The system is intended to produce delay- and phase-corrected dual-polarization channelized voltages in the frequency range 1-3 GHz with a nominal channel bandwidth of 1 MHz. Methods. We used a Radio Frequency System-on-Chip (RFSoC) 4 × 2 evaluation board with four analog-to-digital converters (ADCs) that can simultaneously sample two 1 GHz dual-polarization bands. A critically sampled polyphase filter bank (PFB) using an 8-tap finite impulse response (FIR) filter and a 2048-point fast Fourier transform (FFT) was applied to channelize the input data. Coarse and fine delays were corrected separately before and after the PFB. The post-PFB data were gain corrected before a corner-turner was applied to transpose the channelized data into time-minor order for efficient network transmission. The data were packetized and transmitted over a 100-GbE network. We used Xilinx Vitis HLS C++ to develop the required firmware as a set of customizable modules suitable for rapid prototyping. Results. We performed hardware verification of the channel response of the critically sampled PFB and of the delay correction, showing both to be consistent with theoretical expectations. Furthermore, the board was installed at the Effelsberg 100-meter radio telescope where we performed commensal pulsar observations with the Effelsberg Direct Digitization backend, showing comparable performance. This work demonstrates the utility of high-level synthesis (HLS) languages in the development of high-performance radio astronomy processing backends.

Radio astronomy requires precise target localization and tracking to ensure accurate observations. Conventional regulation methodologies, encompassing PID controllers, frequently encounter difficulties due to orientation inaccuracies precipitated by mechanical limitations, environmental fluctuations, and electromagnetic interferences. To tackle these obstacles, this investigation presents a reinforcement learning (RL)-oriented framework for high-accuracy monitoring in radio telescopes. The suggested system amalgamates a localization control module, a receiver, and an RL tracking agent that functions in scanning and tracking stages. The agent optimizes its policy by maximizing the signal-to-noise ratio (SNR), a critical factor in astronomical measurements. The framework employs a reconditioned 12-m radio telescope at King Mongkut’s Institute of Technology Ladkrabang (KMITL), originally constructed as a satellite earth station antenna for telecommunications and was subsequently refurbished and adapted for radio astronomy research. It incorporates dual-axis servo regulation and high-definition encoders. Real-time SNR data and streaming are supported by a HamGeek ZedBoard with an AD9361 software-defined radio (SDR). The RL agent leverages the Proximal Policy Optimization (PPO) algorithm with a self-attention actor–critic model, while hyperparameters are tuned via Optuna. Experimental results indicate strong performance, successfully maintaining stable tracking of randomly moving, non-patterned targets for over 4 continuous hours without any external tracking assistance, while achieving an SNR improvement of up to 23.5% compared with programmed TLE-based tracking during live satellite experiments with Thaicom-4. The simplicity of the framework, combined with its adaptability and ability to learn directly from environmental feedback, highlights its suitability for next-generation astronomical techniques in radio telescope surveys, radio line observations, and time-domain astronomy. These findings underscore RL’s potential to enhance telescope tracking accuracy and scalability while reducing control system complexity for dynamic astronomical applications.

Abstract Although multimodal large language models (MLLMs) have shown remarkable achievements across various scientific domains, their applications in radio astronomy remain largely unexplored. In this paper, we investigate the potential of MLLMs for image understanding and visual question answering (VQA) in radio astronomy. This can facilitate the use of MLLMs as AI assistants in both research and education by discerning and describing complex astronomical information in human-readable languages. However, general-purpose MLLMs show inferior performance in radio astronomy because they typically lack specialized knowledge. To bridge this gap, we construct a new VQA dataset, RadioAstroVQA, from open data repositories. Specifically, we transform data samples from different repositories into VQA examples by extracting questions based on task descriptions and observation reports associated with images and then composing their answers using ground-truth labels and captions. Furthermore, by leveraging the RadioAstroVQA dataset, we fine-tune two MLLMs of different parameter scales to specifically enhance their capacities for radio astronomical image classification and VQA tasks. Finally, we conduct extensive experiments to show that the fine-tuned MLLMs are capable of handling multiple types of radio astronomical images and generating customized textual output tailored to specific task needs. They achieve accuracy comparable to or even better than that of existing deep learning models for classification tasks. They also demonstrate significantly better performance on VQA tasks compared to several state-of-the-art MLLMs in general domains. These results confirm the potential of MLLMs to serve as specialized AI assistants in the field of radio astronomy.

Narrow-line Seyfert 1 (NLS1) galaxies are a type of active galactic nuclei (AGNs) that had originally been classified as sources with little to no radio emission. Although the class is rather unified from an optical perspective, their radio characteristics are diverse. One of the most curious aspects of these sources is their ability to form and maintain powerful relativistic jets. In this work, we studied the radio properties of the cleanest available sample of 3998 NLS1 galaxies, which allowed us to investigate the population-wide characteristics. We used both historical and ongoing surveys: LOw-Frequency ARray (LOFAR) Two-metre Sky Survey (LoTSS; 144 MHz), Faint Images of the Radio Sky at Twenty-centimeters (FIRST; 1.4 GHz), National Radio Astronomy Observatory (NRAO) Very Large Array (VLA) Sky Survey (NVSS; 1.4 GHz), and VLA Sky Survey (VLASS; 3 GHz). We were able to obtain a radio detection for ∼40% of our sources, with the largest number of detections provided by LoTSS. The majority of the detected NLS1 galaxies are faint (∼1-2 mJy) and non-variable, suggesting considerable contributions from star formation activities, especially at 144 MHz. However, we identified samples of extreme sources, for example, in fractional variability and radio luminosity, indicating significant AGN activity. Our results highlight the heterogeneity of the NLS1 galaxy population in radio, laying the foundation for targeted future studies.

Abstract A key science goal of large sky surveys such as those conducted by the Vera C. Rubin Observatory and precursors to the Square Kilometre Array is the identification of variable and transient objects. One approach is analyzing time series of the changing brightness of sources, namely, light curves. However, finding adequate statistical representations of light curves is challenging because of the sparsity of observations, irregular sampling, and nuisance factors inherent in astronomical data collection. The wide diversity of objects that a large-scale survey will observe also means that making parametric assumptions about the shape of light curves is problematic. We present a Gaussian process (GP) regression approach for characterizing light-curve variability that addresses these challenges. Our approach makes no assumptions about the shape of a light curve and, therefore, is general enough to detect a range of variable and transient source types. In particular, we propose using the joint distribution of GP amplitude hyperparameters to distinguish variable and transient candidates from nominally stable ones and apply this approach to 6394 radio light curves from the ThunderKAT survey. We compare our results with two variability metrics commonly used in radio astronomy, namely η ν and V ν , and show that our approach has better discriminatory power and interpretability. Finally, we conduct a rudimentary search for transient sources in the ThunderKAT data set to demonstrate how our approach might be used as an initial screening tool. Computational notebooks in Python and R are available to help deploy this framework to other surveys.

Vision foundation models, which have demonstrated significant potential in many multimedia applications, are often underutilized in the natural sciences. This is primarily due to mismatches between the nature of domain-specific scientific data and the typical training data used for foundation models, leading to distribution shifts. Scientific data often differ substantially in structure and characteristics, and researchers frequently face the challenge of optimizing model performance with limited labeled data of only a few hundred or thousand images. This work evaluates the performance of vision foundation models in astrophysics, with a focus on identifying the best practices for adapting these models to domain-specific datasets. We aim to establish a framework for selecting, fine-tuning, and optimizing these models for common tasks in optical and radio astronomy. We compared multiple foundation models, including self-supervised, weakly supervised, and distillation-based architectures, across two representative optical and radio datasets. Experiments involved different fine-tuning strategies, projector heads, and data preprocessing techniques, with performance evaluated on classification and detection metrics. Features extracted by specific foundation models improved classification accuracy for optical galaxy images compared to conventional supervised training. Similarly, these models achieved equivalent or superior performance in object detection tasks with radio images. However, classification performance for radio galaxy images was generally poor, often falling short of traditional supervised approaches. These findings suggest that selecting suitable vision foundation models for astrophysics applications requires careful consideration of the model characteristics and alignment with the specific requirements of the downstream tasks. This study demonstrates that vision foundation models can be effectively adapted to astrophysical applications, provided practitioners iterate on model selection, training strategies, and data handling. The proposed framework bridges the gap between these advanced models and the unique demands of astronomy, enabling broader adoption of deep learning in the field.

Radiometers are crucial instruments in radio astronomy, forming the primary component of nearly all radio telescopes. They measure the intensity of electromagnetic radiation, converting this radiation into electrical signals. A radiometer’s primary components are an antenna and a Low Noise Amplifier (LNA), which is the core of the “receiver” chain. Instrumental effects introduced by the receiver are typically corrected or removed during calibration. However, impedance mismatches between the antenna and receiver can introduce unwanted signal reflections and distortions. Traditional calibration methods, such as Dicke switching, alternate the receiver input between the antenna and a well-characterised reference source to mitigate errors by comparison. Recent advances in Machine Learning (ML) offer promising alternatives. Neural networks, which are trained using known signal sources, provide a powerful means to model and calibrate complex systems where traditional analytical approaches struggle. These methods are especially relevant for detecting the faint sky-averaged 21-cm signal from atomic hydrogen at high redshifts. This is one of the main challenges in observational Cosmology today. Here, for the first time, we introduce and test a machine learning-based calibration framework capable of achieving the precision required for radiometric experiments aiming to detect the 21-cm line.Supplementary InformationThe online version contains supplementary material available at 10.1038/s41598-025-16732-9.

Context . Over the past decade, fast radio bursts (FRBs) have attracted substantial interest in the field of astrophysics due to their extremely energetic nature, drawing considerable speculation regarding the mechanisms that are behind these fast transient events. To further our understanding of FRBs, it is essential to develop fast and efficient analysis pipelines to recover more of these events in radio astronomy observations. Aims . We developed a fast end-to-end deep learning based FRB detection pipeline capable of handling ~100 Gb/s of real-time data throughput without applying dedispersion techniques. Methods . We introduced a modified masked ResNet-38 model designed for FRB detection tasks. Using synthetic injections, we demonstrated that our trained end-to-end model matches and surpasses current established pipelines (on injections) with a 7% gain in accuracy without the need for dedispersion or radio frequency interference masking. We deployed this model in a real-time setting at the Allen Telescope Array. Utilizing Nvidia Holoscan, a new GPU-accelerated sensor processing platform along with model optimizations, our pipeline successfully executed an end-to-end FRB detection on beam-formed spectrograms. Results . We report that our end-to-end pipeline achieves a latency of 150× faster than real-time production constraints compared to current state-of-the-art dedispersion + ML assisted FRB search pipelines at the Allen Telescope Array, which is three times slower than real-time constraints. We demonstrate the full functionality of our pipeline by successfully recovering giant pulses from PSR B0531+21 in a real-time setting as well as from FRB20240114 A in an offline setting. This study highlights the promise of future real-time deep-learning-accelerated radio astronomy.

A Closed-Form Subspace Calibration Algorithm for Large Radio Astronomical Phased Arrays

Abstract Sir Francis Graham-Smith dedicated his long life to astrophysics and became a pioneer of radio astronomy

Abstract In this paper, we study 11 pulsars that were discovered by the Five-hundred-meter Aperture Spherical radio Telescope (FAST) in the Commensal Radio Astronomy FAST Survey. We report their phase-connected timing ephemeris, polarization pulse profiles, and Faraday rotation measures. The timing analysis shows that our sample includes seven normal pulsars and four (PSRs J0245+3219, J1437+3049, J1759−1248, and J1813−0852) millisecond pulsars, with periods ranging from 3.15 ms to 2.54 s, characteristic ages from 5.48 × 105 to 1.06 × 1010 yr, and surface magnetic fields from 1.46 × 108 to 1.18 × 1013 G. Six normal pulsars exhibit rich radiation characteristics, including nulling and subpulse drifting. PSR J0245+3219 (30.93 ms) has a spin period, characteristic age, surface magnetic field, and galactic height that are consistent with the parameters of disrupted recycled pulsars. PSR J1813−0852 (4.23 ms) is in a binary system with an orbit of 83.47 days, an eccentricity of 2.9 × 10−4, and its companion has a mass ranging from 0.28 to 0.76 M ⊙. PSR J1813−0852 exhibits a spin period, companion mass, and orbital eccentricity that align well with the predicted characteristics of a millisecond pulsar–helium white dwarf systems.

This paper presents the results of an experimental study of the characteristics of the 3P398A-5 microwave field-effect transistor at a cryogenic temperature of 4 K, aimed at optimizing its use in cryogenic low-noise amplifiers for quantum computing and radio astronomy applications. Current-voltage (I-V) characteristics and S-parameters of the transistor were measured using a specialized measurement setup, which included an Entropy He7 dilution refrigerator, a Keithley 2636B source-measure unit, and a Rohde & Schwarz ZVL13 vector network analyzer. At 4 K, the transistor demonstrated significant performance improvements compared to room temperature: the pinch-off voltage decreased from –2,5 to –0,9 V, the drain current increased from 36 mA to 55 mA, the channel resistance decreased from 83 Ohm to 20 Ohm, and the leakage current was reduced from 10 µA to 1 µA. Analysis of the S-parameters revealed an optimal operating point (Vgs = –0.45 V, Vds = 0.5 V), providing maximum gain (S21) in the 1–12 GHz range at minimal power consumption (approximately 4 mW). The obtained results allow for refinement of transistor models used in the design of high-sensitivity cryogenic amplifiers with low noise and high energy efficiency.

ABSTRACT The cosmic dipole measured in surveys of cosmologically distant sources is generally found to be in disagreement with the kinematic expectation of the cosmic microwave background (CMB). This discrepancy represents severe tension with the Cosmological Principle and challenges the standard model of cosmology. Here, we present a Bayesian analysis of the tension between data sets used to measure the cosmic dipole. We examine the National Radio Astronomy Observatory VLA Sky Survey (NVSS), the Rapid ASKAP (Australian Square Kilometre Array Pathfinder) Continuum Survey (RACS), and the Wide-field Infrared Survey Explorer catalogue (CatWISE), and jointly analyse them with the Planck observations of the CMB. Under the kinematic interpretation, we find that Planck is in severe tension with CatWISE above $5\sigma$, strong tension with RACS, and moderate tension with NVSS. Moreover, the strong concordance between CatWISE and NVSS suggests that their dipoles arise from a common astrophysical signal. Conversely, the high discordance between RACS and both CatWISE and NVSS indicates a possible systematic difference in the RACS catalogue itself. Whilst the tension between Planck and infrared-selected quasars is already significant, the question of whether or not the dipole in individual radio surveys adds to the challenge against the standard model is yet to be seen. We estimate that $\mathcal {O}(10^6)$ radio sources are required to measure the tension to a significance of $5\sigma$. Therefore, in light of the upcoming Square Kilometre Array radio surveys, we are on the cusp of disentangling the anomaly of the cosmic dipole.

Precise Frequencies of H216O Lines Protected for Radio Astronomy

Abstract Deconvolution in radio interferometry faces challenges due to incomplete sampling of the visibilities in the spatial frequency domain caused by a limited number of antenna baselines, resulting in an ill-posed inverse problem. Reconstructing dirty images into clean ones is crucial for subsequent scientific analysis. To address these challenges, we propose an U-Net based method that extracts high-level information from the dirty image and reconstructs a clean image by effectively reducing artifacts and sidelobes. The U-Net architecture, consisting of an encoder-decoder structure and skip connections, facilitates the flow of information and preserves spatial details. Using simulated data of radio galaxies, we train our model and evaluate its performance on the testing set. Compared with the CLEAN method and the Visibility and Image Conditioned Denoising Diffusion Probabilistic Model (VIC-DDPM), our proposed model can effectively reconstruct both extended sources and faint point sources with higher values in the Structural Similarity Index Measure (SSIM) and the Peak Signal-to-Noise Ratio (PSNR). Furthermore, we investigate the impact of noise on the model performance, demonstrating its robustness under varying noise levels.

Abstract Ionospheric scintillation behaves as the random fluctuation of amplitude and phase of the traveling electromagnetic wave caused by irregularities of the Earth ionosphere. In the radio waveband, it influences the performance of satellite navigation systems and radio astronomy observations. Here, the 3.2-meter radio telescope located at Sun Yet-sen University (SYSU 3.2-m radio telescope) in Zhuhai is used to observe the radio signal from the geosynchronous Earth orbit (GEO) satellite C03 of the Beidou navigation system at 1561.098±3 MHz. Fluctuations of intensity in the dynamic spectra, i.e. the standard deviation S4 index, are analyzed to study the scintillation of the ionosphere. The results are compared with those from global navigation satellite system (GNSS) receivers and a GPStation-6 scintillation monitor located at the same place. GNSS receiver and GPStation-6 observations confirm the scintillation events observed by the SYSU 3.2-m radio telescope. The radio telescope observations provide insights into the impact of ionospheric scintillation on astronomical observations.

To construct an interferometer with a baseline spanning the planet, US radio astronomers reached out to their Soviet counterparts.

This study presents a convex optimization framework for beam synthesis in Square Kilometre Array low-frequency radio telescope stations configured in a sunflower-like layout. The method minimizes the peak sidelobe level by computing an optimized set of beamforming weights, enabling precise control of the main beam while preserving angular resolution. The framework is validated through full-wave electromagnetic simulations based on detailed physical models of the antenna elements and station geometry. Compared to conventional beamforming employing constant unitary real weights, the optimized solutions yield a significant reduction in sidelobe levels, with only a minimal impact on directivity. These benefits are particularly evident at frequencies where mutual coupling between array elements is strong, confirming the suitability of the proposed approach for dense radio astronomy arrays.