Radio-interferometric arrays have become a crucial tool in astronomical observation, playing a vital role in many fields such as astronomy and astrophysics. These instruments synthesize virtual large apertures through interferometric techniques using sparsely distributed small antennas, achieving unprecedented angular resolution. However, since the observation data of radio-interferometric arrays only cover part of the spatial frequencies, the imaging process is essentially an ill-posed inverse problem. Conventional methods like CLEAN algorithms have limitations in image quality and scalability to massive datasets. Although compressed sensing (CS) has exhibited remarkable performance in radiointerferometric imaging, existing CS-based radio-interferometric imaging algorithms still suffer from the following drawbacks: in terms of sparse bases, they have insufficient sparse representation capability, especially in characterizing the structural features of radio images; in terms of optimization algorithms, they adopt L 1-norm minimization to approximately replace the intractable L 0-norm minimization problem, which inevitably introduces reconstruction bias and compromises imaging fidelity, particularly for astronomical sources with high dynamic range. To address this issue, this paper proposes a high-precision and fast imaging method based on the curvelet transform. First, we employ the fast discrete curvelet transform (FDCT) via the wrapping algorithm to sparsely represent astronomical images, which provides a multi-scale and multi-directional analysis capability that effectively captures structural features such as edges and curved filaments with remarkable efficiency. Compared to traditional wavelet-based methods, the FDCT substantially enhances the quality of sparse representation. Second, the smoothly clipped absolute deviation (SCAD) penalty is adopted instead of the conventional L 1 norm to more accurately approximate the ideal L 0 norm, thus reducing reconstruction bias in sparse recovery while maintaining computational tractability. Building on this formulation, an improved proximal gradient algorithm is developed to solve the non-convex optimization problem. Moreover, the inertia-start and gradient-based restart strategies are incorporated into the iterative process to effectively suppress oscillations and accelerate convergence. Furthermore, the maximum likelihood estimation is used to adaptively select the regularization parameter during iterations, eliminating the need for manual parameter tuning and enhancing the robustness of the algorithm. Several simulations and real-data experiments have been conducted using the Very Large Array (VLA) and the DAocheng Radio Telescope (DART). The experimental results show that the proposed method is significantly superior to the adaptive scale pixel decomposition algorithm (Asp-CLEAN) in terms of reconstruction accuracy evaluated by signalto-noise ratio and image domain data fidelity, though it is slower in computational speed than Asp-CLEAN. Besides, the proposed method outperforms existing compressed sensing algorithms such as isotropic undecimated wavelet transform (IUWT), artificial intelligence for regularization in radio-interferometric imaging (AIRI) and Lq proximal gradient (LPG) in both reconstruction accuracy and computational efficiency, thus verifying its effectiveness. These results offer a promising new approach for efficient and high-quality imaging of future large-scale radio-interferometric arrays such as the Square Kilometre Array (SKA).

Reconstructing the sky brightness distribution from the incomplete visibilities involves an ill-posed inverse problem. Although compressive sensing methods based on convex optimization have demonstrated outstanding performance in radio interferometry, convex optimization yields a computable but biased approximate solution for compressive sensing. To reduce the bias and efficiently obtain an accurate solution, we proposed an imaging algorithm based on generalized minimax-concave penalty, which maintains the convexity of the sparsity-regularized least-squares objective function. Furthermore, we employ the forward-backward splitting algorithm to solve the optimization problem and adaptively update the regularization parameter by using a maximum likelihood estimator. We have verified the effectiveness of the proposed method based on the Very Large Array and DAocheng Radio Telescope.

In radio interferometry, the process of reconstructing the signal from measured visibilities is an ill-posed inverse problem. Although the compressive sensing (CS) technology has been successfully applied in radio interferometry, the conventional CS methods use a fixed set of sparse bases for the entire astronomical image, which cannot achieve a sufficiently high degree of sparsity and thus affects the performance of CS reconstruction. This paper proposes a novel radio-interferometric imaging algorithm based on adaptively learned sparsifying basis. Considering that many signals in the universe are nonstationary, the proposed method uses an adaptive learning dictionary to sparsely represent overlapped image patches, which can effectively capture local features of the image and reduce the artifacts. Furthermore, the split Bregman iteration algorithm is employed to alternately solve the dictionary learning and image estimation. The effectiveness of the proposed method is validated through multiple simulation experiments.

Abstract In this paper, we study a type I burst chain exhibiting a negative frequency drift. This event was observed simultaneously on 6 August 2023 by the Daocheng Radio Telescope (DART) and Chashan broad‐band solar radio spectrometer at meter wavelengths (CBSm). It lasted for 174 s, with an average drift rate of −0.48 MHz from 241 to 157 MHz. Based on the fundamental and harmonic emissions of plasma mechanism, this type I burst chain displays its source motion upward with a velocity from 120 km to 540 km after fitting its dynamic spectra with both Newkirk and Aschwanden electron density models. During its lifetime, the source's total displacement of 21–94 Mm is below the DART spatial resolution, resulting into no motion seen on DART images. It is interesting that a mini‐jet is observed simultaneously by SDO/AIA in the radio emission region with a speed of 296 km , which is consistent with the radio source upward motion velocities detected here, indicating that the type I burst chain could possibly originate from the nonthermal electrons accelerated by a moving magnetic reconnection following this mini‐jet. These findings would help us to improve the understanding of the characteristics and origin of type I bursts.

The process of reconstructing the brightness temperature images of the observed scene from the measured visibility function is an ill-posed inverse problem in synthetic aperture interferometric radiometers (SAIRs). The disadvantages of model-based approaches widely used are slow calculation speed for large SAIR arrays, and large residual errors and oscillation ripples due to the incomplete coverage of frequency information. In this article, we propose a novel SAIR reconstruction approach based on deep generalized unfolding network with good interpretability. The proposed method learns the mapping relation from the measured visibilities to the image by unrolling the proximal gradient descent algorithm into a deep network. To improve the performance of the model, the proposed method improves the downsampling module by incorporating the Haar wavelet transform. Visual inspection and quantitative analysis demonstrate that the proposed method has superior performance in terms of reconstruction quality, noise suppression and computation speed.

Long-period radio transients (LPTs) are a newly discovered class of radio emitters with periods ranging from minutes to hours. The astrophysical nature remains undetermined, particularly of LPTs with no detectable companions. We report the first evidence for a plausible supernova remnant (SNR) association with an LPT (DART J1832-0911, 2656.23 ± 0.15 s period), which supports a neutron star origin of such objects. The dispersion measure of this LPT, SNR's CO emission and neutral hydrogen (HI) absorption, and low probability of chance of alignment with field pulsars are all consistent with such an association. The source displays either phase-locked circular or nearly 100% linear polarization, indicating its strong and geometrically stable magnetic field. No detectable optical counterpart was found, even with a 10 m-class telescope. The SNR association and the stable polarization suggest that DART J1832-0911 most likely originates from a young neutron star, whose spin could have been braked by supernova's fallback materials. This discovery provides critical insights into the nature of ultra-long period transients and their link to stellar remnants.

Filament eruptions are considered to be a common phenomenon on the Sun and other stars, yet they are rarely directly imaged in the meter and decimeter wavebands. Using imaging data from the DAocheng solar Radio Telescope (DART) in the 150-450 MHz frequency range, we present two eruptive filaments that manifest as radio dimmings (i.e., emission depressions). Simultaneously, portions of these eruptive filaments are discernible as dark features in the chromospheric images. The sun-as-a-star flux curves of brightness temperature, derived from the DART images, exhibit obvious radio dimmings. The dimming depths range from 1.5% to 8% of the background level and show a negative correlation with radio frequencies and a positive correlation with filament areas. Our investigation suggests that radio dimming is caused by free-free absorption during filament eruptions obscuring the solar corona. This may provide a new method for detecting stellar filament eruptions.

Context. Reconstructing a high-resolution image of observed radio sources from the incomplete visibilities poses a challenging, ill-posed, inverse problem. Although compressive sensing has demonstrated remarkable performance in radio interferometric imaging, traditional compressed sensing methods approximately replace the L-0-norm minimisation problem with the L-1-norm minimisation problem, which brings about a bias issue. Aims. To ameliorate the bias problem and efficiently obtain an accurate solution in radio interferometry, we propose a novel, non-convex sparse regularisation method based on smoothly clipped absolute deviation (SCAD) in this paper. Methods. The proposed method utilises the continuous SCAD penalty function to approximate the L-0 norm and efficiently solves the non-convex optimisation problem by using an improved proximal gradient algorithm. The improved proximal gradient algorithm introduces a restart strategy and an adaptive non-monotonic step-size strategy to improve the convergence speed of the algorithm. Moreover, the regularisation parameter was adaptively updated using the prior information of the image. Results. Numerical simulation experiments are carried out on the Very Large Array (VLA) and Square Kilometre Array (SKA). We compare the proposed method with state-of-the-art imaging methods. The results show thatitperforms better in terms of reconstruction quality and computational efficiency.

We explored the quasi-periodic pulsations (QPPs) at multiple periods during an X4.0 flare on 2024 May 10 (SOL2024-05-10T06:27), which occurred in the complex active region of NOAA 13664. The flare radiation reveals five prominent periods in multiple wavelengths. A 8-min QPP is simultaneously detected in wavelengths of HXR, radio, UV/EUV, Lya, and white light, which may be associated with nonthermal electrons periodically accelerated by intermittent magnetic reconnection that is modulated by the slow wave. A quasi-period at 14 minutes is observed in the SXR and high-temperature EUV wavebands, and it may be caused by repeatedly heated plasmas in hot flare loops. A quasiperiod at about 18 minutes is only observed by STIX, with reconstructed SXR images suggesting that the 18-min period pulsations should be considered as different flares. Meanwhile, a 3-min QPP is simultaneously detected in wavelengths of HXR, radio, and UV/ EUV, which is directly modulated by the slow magnetoacoustic wave leaking from sunspot umbrae. At last, a 2-min QPP is simultaneously detected in HXR and radio emissions during the pre-flare phase, which is possibly generated by a quasi-periodic regime of magnetic reconnection that is triggered by the kink wave.

The reconstruction from the measured visibilities to the signal in radio interferometry is an ill-posed inverse problem. The compressed sensing technology represented by the sparsity averaging reweighted analysis (SARA) has been successfully applied to radio-interferometric imaging. However, the traditional SARA algorithm solves the L 1 norm minimization problem instead of the L 0 norm one, which has a bias problem. In this paper, a L q proximal gradient algorithm with 0 < q < 1 is proposed to ameliorate the bias problem and obtain an accurate solution in radio interferometry. The proposed method efficiently solves the L q norm minimization problem by using the proximal gradient algorithm, and adopts restart and lazy-start strategies to reduce oscillations and accelerate the convergence rate. Numerical experiment results and quantitative analyses verify the effectiveness of the proposed method.

Reconstructing the signal from measured visibilities in radio interferometry is an ill-posed inverse problem. In this paper, we present a novel radio-interferometric imaging method based on the wavelet tight frame aimed at efficiently obtaining an accurate solution. In our approach, the signal is sparsely represented by the directional tensor product complex tight framelets, which can effectively capture the texture and shape features of the images. To enhance computational efficiency, we employ the projected fast iterative soft-thresholding algorithm for solving the l _1 -norm minimization problem. Several simulation experiments are carried out to verify the effectiveness and performance of the proposed method.

Reconstructing the brightness temperature map from the visibilities in synthetic aperture interferometric radiometers (SAIRs) has been proved to be an ill-posed inverse problem. The regularization methods are crucial to effectively overcome the ill-condition of the inverse problem. This letter presents a maximum a posteriori (MAP) approach for retrieving the accurate brightness temperature map in SAIRs. Furthermore, edge-preserving regularization using a Huber Markov Random Field (Huber-MRF) model is used to provide a stable solution and eliminate the oscillation artifacts. Numerical experiment results and quantitative analyses verify the effectiveness of the proposed method.

Since fast head-on coronal mass ejections and their associated shocks represent potential hazards to the space environment of the Earth and even other planets, forecasting the arrival time of the corresponding interplanetary shock is a priority in space weather research and prediction. Based on the radio spectrum observations of the 16-element array of the Daocheng Solar Radio Telescope (DSRT), the flagship instrument of the Meridian Project of China, during its construction, this study determines the initial shock speed of a type II solar radio burst on 2022 April 17 from its drifting speed in the spectrum. Assuming that the shock travels at a steady speed during the piston-driven phase (determined from the X-ray flux of the associated flare) and then propagates through interplanetary space as a blast wave, we estimate the propagation and arrival time of the corresponding shock at the orbit of the Solar Terrestrial Relations Observatory-A (STEREO-A). The prediction shows that the shock will reach STEREO-A at 14:31:57 UT on 2022 April 19. The STEREO-A satellite detected an interplanetary shock at 13:52:12 UT on the same day. The discrepancy between the predicted and observed arrival time of the shock is only 0.66 hr. The purpose of this paper is to establish a general method for predicting the shock’s propagation and arrival time from this example, which will be utilized to predict more events in the future based on the observations of ground-based solar radio spectrometers or telescopes like DSRT.

Synthetic aperture radio telescopes are important in the field of radio astronomy. However, the inverse imaging process of these telescopes is an ill-posed inverse problem. Although the compressed sensing technology represented by the sparsity averaging reweighted analysis (SARA) algorithm has been successfully applied to the imaging of the synthetic aperture radio telescope, large reconstruction errors remain in the traditional SARA algorithm due to its difficulty in selecting soft threshold parameters. Therefore, an improved SARA algorithm is proposed. This algorithm uses an improved projected fast iterative soft thresholding algorithm to solve the minimization model, adaptively updates soft threshold parameters by fully utilizing data fidelity and regularization terms, and adopts restarting and adaptive strategies to accelerate convergence, thereby improving the overall accuracy and speed of inversion imaging. The simulation results demonstrate that compared with the traditional compressed sensing algorithm, the improved SARA algorithm can effectively reduce reconstruction error and increase calculation speed, thus proving its effectiveness.

Daocheng Solar Radio Telescope (DSRT) is an important part of the solar interplanetary exploration subsystem of the Chinese Meridian Project-Phase II. It operates in the 150−450 MHz frequency band, providing high-time, high-angular resolution images of the solar radio burst. Aiming at the high precision pointing measurement of the DSRT antenna and the requirement of batch calibration of pointing errors, the antenna's 3-parameter encoder zero-point offset pointing error model is established by quaternion rotation transformation method according to the unique 3-axis mount system of the DSRT. Then, the drift scanning method based on radio source is proposed to obtain the radiation power pattern of the 16 antennas, and the pointing errors are accurately measured by determining the boresight according to the 2-dimensional power pattern. Finally, the least square method is used to fit the model parameters, and the encoder zero point is readjusted for each axis via antenna control software. Verification of the adjustment results confirmed the reliability and effectiveness of the pointing calibration method. After correction, the pointing accuracy of 16 antennas is within 0.5∘, which is significantly better than the pointing error of 3.5∘ before calibration, meeting the requirement that errors are less than 1/10 of the HBPW (half power beam width) at the highest operating frequency of the DSRT antenna.

Understanding the internal structure of asteroids is crucial for deciphering their formation and establishing defenses against potential hazards. The Daocheng Radio Telescope (DART), a recently constructed interferometric array designed for low-frequency Sun imaging, presents a promising tool for probing asteroid interiors. With a substantial 1-km array aperture and an equivalent receiving area of approximately 8,850 m2, DART plays a vital role in diagnosing asteroid internal structures. This study introduces an electromagnetic wave scattering model tailored to asteroids within DART’s operational frequency range (150 to 450 MHz). Ground-based radar detection can unveil multiple facets of these celestial bodies by leveraging low-frequency waves’ penetrating capabilities and capitalizing on asteroids’ rotational dynamics. Through simulations capturing the characteristics of low-frequency waves traversing a layered model and interacting with internal structures, we propose an electromagnetic scattering model of asteroids. Our results underscore DART’s potential as a crucial instrument for discerning the internal structure of near-Earth objects. We first formulate an asteroid model through celestial impact models, dimensional analysis, and data fitting to achieve this. Subsequently, we derive an electromagnetic scattering model using geometric optics and a propagation model for lossy mediums. Simulations demonstrate that morphology and internal structure dictate the distribution of scattered waves, with forward and backscattered waves providing comprehensive internal structure information over a rotation cycle. Furthermore, we observe that alterations in electromagnetic wave frequency induce changes in the scattering characteristics, prompting the convenience of employing multiple frequencies for retrieving detailed information about an asteroid’s internal medium and structure. This multidimensional approach positions DART as a promising asset in advancing our understanding of asteroid interiors, offering valuable insights for scientific inquiry and hazard mitigation strategies.