Solar Radio Observations Research Articles

First determination of the angular dependence of rise and decay times of solar radio bursts using multi-spacecraft observations

A large arsenal of space-based and ground-based instruments is dedicated to the observation of radio emissions, whether they originate within our solar system or not. Radio photons interact with anisotropic density fluctuations in the heliosphere which can alter their trajectory and influence the properties that are deduced from observations. This is particularly evident in solar radio observations, where anisotropic scattering leads to highly directional radio emissions. Consequently, observers at varying locations will measure different properties, including different source sizes, source positions, and intensities. However, it is not known whether the measurements of the decay time of solar radio bursts are also affected by the observer’s position. Decay times are dominated by scattering effects, and so are frequently used as proxies of the level of density fluctuations in the heliosphere, making the identification of any location-related dependence crucial. We combine multi-vantage observations of interplanetary Type III bursts from four non-collinear, angularly separated spacecraft with simulations to investigate the dependence of the decay- and rise-time measurements on the separation of the observer from the source. We propose a function to characterise the entire time profile of radio signals, allowing for the simultaneous estimation of the peak flux, decay time, and rise time, while demonstrating that the rise phase of radio bursts is non-exponential, having a non-constant growth rate. We determine that the decay and rise times are independent of the observer’s position, identifying them as the only properties that remain unaffected and thus do not require corrections for the observer’s location. Moreover, we examine the ratio between the rise and decay times and find that it does not depend on the frequency. Therefore, we provide the first evidence that the rise phase is also significantly impacted by scattering effects, adding to our understanding of the plasma emission process.

Semantic Segmentation of Solar Radio Spikes at Low Frequencies

Solar radio spikes are short lived, narrow bandwidth features in low frequency solar radio observations. The timing of their occurrence and the number of spikes in a given observation is often unpredictable. The high temporal and frequency of resolution of modern radio telescopes such as NenuFAR mean that manually identifying radio spikes is an arduous task. Machine learning approaches to data exploration in solar radio data is on the rise. Here we describe a convolutional neural network to identify the per pixel location of radio spikes as well as determine some simple characteristics of duration, spectral width and drift rate. The model, which we call SpikeNet, was trained using an Nvidia Tesla T4 16GB GPU with ~100000 sample spikes in a total time of 2.2 hours. The segmentation performs well with an intersection over union in the test set of ~0.85. The root mean squared error for predicted spike properties is of the order of 23%. Applying the algorithm to unlabelled data successfully generates segmentation masks although the accuracy of the predicted properties is less reliable, particularly when more than one spike is present in the same 64 X 64 pixel time-frequency range. We have successfully demonstrated that our convolutional neural network can locate and characterise solar radio spikes in a number of seconds compared to the weeks it would take for manual identification.

An Assessment of Solar Cycle 25 progress through observation of SRBs and associated Geomagnetic Storms

Geomagnetic storms are severe aspects of Space Weather. They originate due to solar transient emissions such as coronal mass ejections (CMEs), whose energetic materials propagate in the Interplanetary medium and are coupled with the magnetosphere system. CME driven Geomagnetic storms are often associated with solar radio bursts (SRBs), particularly type II and type IV bursts. In this study, we present the preliminary results of solar radio observations and their associated geomagnetic activity during solar cycle 25 (SC 25) from January 2020 to June 2023, focusing on the cycle’s first four intense geomagnetic storms. The study used various radio telescopes, mainly the compound astronomical low-frequency low-cost instrument for spectroscopy and transportable observatory (CALLISTO), as well as OMNI data and the World Data Center for Geomagnetism. During the study period, it was found that 23 solar radio bursts diagnosed the geomagnetic storms with Dst <-50nT from 35 reported, including three severe storms of the SC 25. The time delay between the solar radio bursts and the arrival of CMEs and/or HSS near the Earth’s magnetosphere is estimated with an average value of 79 h within the [48–120 h] range for 23 geomagnetic storms associated with solar radio bursts. Among 35 geomagnetic storms recorded, five are recurring geomagnetic storms associated with coronal high-speed streams (HSS), while CMEs cause the rest with average speeds of 750 km/s. The current SC 25 recognizes four major storms within the scope of the study. On 21 April 2023, a type II radio burst followed by a type IV burst diagnosed the first severe geomagnetic storm on 24 April 2023. The second severe storm was unusual and detected in the absence of the precursor as a solar radio burst. The SRBs of type II burst and type IV burst extending in IP medium on 1 November 2021 tracked the third major storm of the cycle while the group of type III radio bursts, type II and type IV bursts on 24 February 2023, predicted the major storm on 27 February 2023. These major geomagnetic storms are linked to CMEs that show expanding flux ropes, which are signatures of type II and moving type IV radio bursts identified. Furthermore, the detected SRBs and related major geomagnetic storms are proof of high solar and magnetic activity of the ascending phase of SC 25. The SC 25 has been characterized overall, and its current progress is being tracked using observations of SRBs and magnetic activity during its rising phase.

Solar radio spectrogram segmentation algorithm based on improved fuzzy C-means clustering and adaptive cross filtering

Solar radio spectrograms contain essential information, such as the duration type; therefore, recognizing and detecting solar radio spectrograms are significant for the further study of solar radio. With the upgrading of solar radio observation, considering the equipment that has already generated amounts of data, researchers have begun to use machine learning methods to recognize and detect solar radio spectrograms to resolve the weaknesses of manual identification, such as time consumption. However, the spectrograms are characterized by noise or insignificant outburst features, which affect the recognition and detection of solar radio spectrograms. In contrast, extracting the burst region separately and the more distinctive spectrogram features will help identify and detect it. Therefore, to remove the burst domain of the radio spectrogram better, this paper combines the idea of image segmentation and proposes a solar radio spectrogram segmentation algorithm based on improved fuzzy C-means (FCM) clustering and adaptive cross filtering for the extraction of the burst domain of solar radio spectrograms. This algorithm has multiple processing steps. The first step is solar radio spectrogram segmentation with the improved FCM based on the kernel-induced distance by incorporating spatial constraints combined with random walk and adaptive affiliation linking (RWAKFCM_S). The second step is adaptive cross filtering, eliminating the noise clustered in bursts. The results show the following. (1) The RWAKFCM_S proposed in this paper has better anti-noise and segmentation performance than other methods in the synthetic, natural, and solar radio spectrogram segmentation experiments; it can also overcome the problems of noise sensitivity when segmenting spectrograms by traditional FCM. (2) The RWAKFCM_S can satisfy the high accuracy and rate of solar radio spectrogram segmentation demands. (3) The adaptive cross filtering proposed in this paper can eliminate noise clustered in the eruption domain. (4) The proposed method enables burst region extraction.

Observation of solar radio burst events from Mars orbit with the Shallow Radar instrument

Context. Multispacecraft and multiwavelength observations of solar eruptions, such as flares and coronal mass ejections, are essential to understanding the complex processes behind these events. The study of solar burst events in the radio frequency spectrum has relied almost exclusively on data from ground-based observations and a few dedicated heliophysics missions such as STEREO or Wind. Aims. By reanalysing existing data from the Mars Reconnaissance Orbiter (MRO) Shallow Radar (SHARAD) instrument, a Martian planetary radar sounder, we discovered the instrument was also capable of detecting solar radio bursts and that it was able to do so with unprecedented resolution for a space-based solar instrument. In this study, we aim to demonstrate the reliability and value of SHARAD as a new solar radio observatory. Methods. We characterised the sensitivity of the instrument to type III solar radio bursts through a statistical analysis of correlated observations using STEREO and Wind as references. Using 38 correlated detections, we established the conditions under which SHARAD can observe solar bursts in terms of acquisition geometry. As an example of scientific application, we also present the first analysis of type III characteristic times at high resolution beyond 1 AU. Results. A simple logistic model based purely on geometrical acquisition parameters can predict burst show versus no-show in SHARAD data with an accuracy of 79.2%, demonstrating the reliability of the instrument in detecting solar bursts and laying the foundation for using SHARAD as a solar radio observatory. The extremely high resolution of the instrument, both in temporal and frequency directions; its bandwidth; and its position in the Solar System enable SHARAD to make significant contributions to heliophysics. Notably, it could provide data on plasma processes on the site of the burst generation and along the propagation path of associated fast electron beams.

Relationship of peak fluxes of solar radio bursts and X-ray class of solar flares: Application to early great solar flares

Abstract Large solar flares occasionally trigger significant space-weather disturbances that affect the technological infrastructures of modern civilization, and therefore require further investigation. Although these solar flares have been monitored by satellite observations since the 1970s, large solar flares occur only infrequently and restrict systematic statistical research owing to data limitations. However, Toyokawa Observatory has operated solar radio observations at low frequencies (at 3.75 and 9.4 GHz) since 1951 and captured the early great flares as solar radio bursts. To estimate the magnitudes of flares that occurred before the start of solar X-ray (SXR) observations with the Geostationary Operational Environmental Satellite (GOES) satellites, we show the relationship between microwave fluxes at 3.75 and 9.4 GHz and X-ray fluxes of flares that occurred after 1988. In total, we explored 341 solar flares observed with the Nobeyama Radio Polarimeters and Toyokawa Observatory from 1988–2014 and compared them with the SXR observations recorded by the GOES satellites. The correlation coefficient was approximately 0.7. Therefore, the GOES X-ray class can be estimated from the peak flux at 3.75 and 9.4 GHz with a large variance and an error of factor of 3 (1σ). Thus, for the first time, we quantitatively estimated the light curves of two early solar flares observed in 1956 February by the Toyokawa solar radio observations using the relationship between SXR thermal radiation and microwave nonthermal radiation (Neupert, 1968, ApJ, 153, 59).

Q-band balanced bandpass filter based on rectangular micro-coaxial transmission line with improved common-mode suppression

This paper presents a novel design of a wideband millimeter-wave balanced bandpass filter (BPF) for use in a high-sensitivity Q-band reception system for solar radio spectrum observation. The balanced BPF has differential-mode (DM) band-pass and common-mode (CM) band-stop characteristics, which effectively suppress noise and reduce electromagnetic interference in its operating frequency band. The filter design incorporates half-wavelength rectangular micro-coaxial transmission lines as resonators and coupling between resonators is achieved through J-inverters. The wideband CM suppression is improved by adjusting the distribution of CM transmission zeros by adding open-circuited branches on the symmetrical plane. The micro-coaxial structure is fabricated on a 4-inch silicon wafer through a three-dimensional additive manufacturing process. The compactness, low loss, and wideband performance make it ideal for this application. As a design example, a fifth-order Chebyshev balanced BPF was fabricated and measured to demonstrate the design method. The measured results indicate that the balanced BPF operates within a frequency range of 30–50 GHz, with an insertion loss of 1.63 dB and over 26 dB CM suppression in the DM passband. These results are in general agreement with simulation results. To the best of our knowledge, this is the first time that a micro-coaxial structure has been proposed for this application.

The Importance of Synoptic Solar Radio Observations

The Importance of Synoptic Solar Radio Observations

X-ray Tests of General Relativity with Black Holes

General relativity is one of the pillars of modern physics. For decades, the theory has been mainly tested in the weak-field regime with experiments in the solar system and radio observations of binary pulsars. Until 2015, the strong-field regime was almost completely unexplored. Thanks to new observational facilities, the situation has dramatically changed in the last few years. Today, we have gravitational wave data of the coalesce of stellar-mass compact objects from the LIGO-Virgo-KAGRA collaboration, images at mm wavelengths of the supermassive black holes in M87* and Sgr A* from the Event Horizon Telescope collaboration, and X-ray data of accreting compact objects from a number of X-ray missions. Gravitational wave tests and black hole imaging tests are certainly more popular and are discussed in other articles of this Special Issue. The aim of the present manuscript is to provide a pedagogical review on X-ray tests of general relativity with black holes and to compare these kinds of tests with those possible with gravitational wave data and black hole imaging.

Research on Calibration Sources for a 35–40 GHz Millimeter-Wave Solar Radio Observation System

The microwave spectra in the optically thin regime provides unique information about the solar corona magnetic field; thus, a millimetre-wave solar radio observation system (35-40 GHz) in the Chashan Observatory is newly established to obtain relevant data. Herein, we design and develop a blackbody calibration source employing a metal cone array and absorbing patch to calibrate the system and obtain accurate solar observation data for analysis and decision-making. The reflectivity of the calibration source is simulated and measured to characterize its emissivity. The reflectivity is obtained by the finite-integration (FIT) method under various fitting conditions, i.e., the thickness of the absorbent patches and aspect ratios of the cone. The temperature gradient of cones is determined by the finite-element method (FEM). A 5×5 array microwave blackbody was developed based on a cone with a low scattering coefficient and excellent temperature uniformity. To reduce the influence of spurious signals, the time-domain gate technique is used to test the reflectivity of the target. The results demonstrate that the emissivity of the blackbody is higher than 0.9995 in the 35-40 GHz band. The temperature difference between the cone top and the cone bottom is 0.20 °C.

Research on interference and noise reduction technology of solar radio observation system

The dynamic spectrum of solar radio burst is a very important tool to study the characteristics of solar radio burst. However, due to the influence of the instruments noise of the solar radio telescope, external interference, the change of ambient temperature, absorption of clouds and so on, the sensitivity of the observation system will be reduced. In particular, the weak solar radio burst signal is easily submerged by various interference signals. In order to improve the sensitivity of solar radio telescope to observe solar radio burst signals and remove narrow-band interference signals, a spectrum data processing algorithm of spectral subtraction method based on entropy and energy threshold values (SSM-EE) is proposed in this article. There are multiple processing steps of SSM-EE. The first step is to improve the signal-to-noise ratio (SNR) through accumulating and averaging the spectrum data. The second step is to eliminate narrowband interference. The spectral kurtosis algorithm is used to calculate the spectral kurtosis of the narrowband interference signal, and then whether to smooth it is determined according to whether it is greater than the threshold. The third step is to establish a noise model. Calculating the power spectrum entropy and energy entropy of each frame of the processed spectrum data, judge whether it is a noise signal by judging the relationship between the power spectrum entropy and energy entropy and the corresponding threshold. The update model for noise floor is used to obtain the real-time noise floor data. The last step is to eliminate the background noise through spectral subtraction. The new spectrum data are obtained by subtraction method between the original spectrum data and the noise floor data. After a large number of data simulation and verification of the actual observation data, it shows that the method proposed in this article has good practical value.

Automated detection and statistical study of solar radio spikes

The most typical observational features of solar radio spikes are their short duration and narrow bandwidth. We have improved the YOLOv5s network model for these characteristics by adding inclined bounding frames and attention and feature fusion mechanism modules. The decimeter- and meter-wavelength spikes observed by the Solar Broad-band Radio Spectrometer in Huairou and the Chashan Solar Radio Observatory spectrograph are used to carry out experiments, respectively. The results demonstrate that the AP value obtained by the improved network is 74%, which is almost 14% higher than the original network. The improved network detects 9709 (1379) decimeter- (meter-) wavelength spikes in two events with durations, bandwidths, relative bandwidths, and frequency-drift rates. The spikes at decimeter and meter wavelengths are again categorized based on their frequency-drift rates, such as positive, negative, and no measurable frequency-drift rates. We have carried out a statistical study on these categorized spikes. These statistical results and findings constrain solar radio spikes’ formation.

Classification of Solar Radio Spectrum Based on Swin Transformer

Solar radio observation is a method used to study the Sun. It is very important for space weather early warning and solar physics research to automatically classify solar radio spectrums in real time and judge whether there is a solar radio burst. As the number of solar radio burst spectrums is small and uneven, this paper proposes a classification method for solar radio spectrums based on the Swin transformer. First, the method transfers the parameters of the pretrained model to the Swin transformer model. Then, the hidden layer weights of the Swin transformer are frozen, and the fully connected layer of the Swin transformer is trained on the target dataset. Finally, parameter tuning is performed. The experimental results show that the method can achieve a true positive rate of 100%, which is more accurate than previous methods. Moreover, the number of our model parameters is only 20 million, which is 80% lower than that of the traditional VGG16 convolutional neural network with more than 130 million parameters.

Self-Supervised Learning for Solar Radio Spectrum Classification

Solar radio observation is an important way to study the Sun. Solar radio bursts contain important information about solar activity. Therefore, real-time automatic detection and classification of solar radio bursts are of great value for subsequent solar physics research and space weather warnings. Traditional image classification methods based on deep learning often require considerable training data. To address insufficient solar radio spectrum images, transfer learning is generally used. However, the large difference between natural images and solar spectrum images has a large impact on the transfer learning effect. In this paper, we propose a self-supervised learning method for solar radio spectrum classification. Our method uses self-supervised training with a self-masking approach in natural language processing. Self-supervised learning is more conducive to learning the essential information about images compared with supervised methods, and it is more suitable for transfer learning. First, the method pre-trains using a large amount of other existing data. Then, the trained model is fine-tuned on the solar radio spectrum dataset. Experiments show that the method achieves a classification accuracy similar to that of convolutional neural networks and Transformer networks with supervised training.

The physics of solar spectral imaging observations in dm-cm wavelengths and the application on space weather

Recently, several new solar radio telescopes have been put into operation and provided spectral-imaging observations with much higher resolutions in decimeter (dm) and centimeter (cm) wavelengths. These telescopes include the Mingantu Spectral Radioheliograph (MUSER, at frequencies of 0.4–15 GHz), the Expanded Owens Valley Solar Array (EOVSA, at frequencies of 1–18 GHz), and the Siberian Radio Heliograph (SRH, at frequencies of 3–24 GHz). These observations offer unprecedented opportunities to study solar physics and space weather, especially to diagnose the coronal magnetic fields, reveal the basic nature of solar eruptions and the related non-thermal energy release, particle accelerations and propagation, and the related emission mechanisms. These results might be the important input to the space weather modeling for predicting the occurrence of disastrous powerful space weather events. In order to provide meaningful reference for other solar physicists and space weather researchers, this paper mainly focus on discussing the potential scientific problems of solar radio spectral-imaging observations in dm-cm wavelengths and its possible applications in the field of space weather. These results will provide a helpful reference for colleagues to make full use of the latest and future observation data obtained from the above solar radio telescopes.

DLITE—An inexpensive, deployable interferometer for solar radio burst observations

Solar radio bursts (SRBs) are brief periods of enhanced radio emission from the Sun. SRBs can provide unique insights into the plasma structure where emission occurs. SRBs can also provide critical information concerning space weather events such as coronal mass ejections or solar energetic particle events. Providing continuous monitoring of SRBs requires a full network of detectors continuously monitoring the Sun. A promising new network is being developed, employing a four-element interferometer called the Deployable Low-band Ionosphere and Transient Experiment (DLITE) array. DLITE, which operates in a 30–40 MHz band, was specifically designed to probe the Earth’s ionosphere using high resolution measurements (1.024-s temporal resolution, 16.276-kHz frequency resolution); however, this also makes DLITE a powerful new tool for providing detailed observations of SRBs at these frequencies. DLITE is particularly adept at detecting long-duration SRBs like Type II and Type IV bursts. DLITE provides high resolution SRB data that can complement ground-based networks like e-Callisto or space-based observations, e.g., from Wind/WAVES. As an inexpensive interferometer, DLITE has strong potential as an educational tool: DLITE can be used to study the ionosphere, SRBs, and even Jovian radio bursts. Future DLITE arrays could be enhanced by using the full 20–80 MHz band accessible by the antennas and employing its millisecond time-resolution capability; this would improve DLITE’s ability to track long-duration bursts, create the opportunity to study short-duration Type III bursts in detail, and, in particular, make the study of Type I bursts practical.

A 3 Giga Sample Per Second 14-bit Digital Receiver with 9 GHz Input Bandwidth for Solar Radio Observation

A new digital receiver with excellent performances has been designed and developed for solar radio observation, which can receive the radio signal from direct current (DC) to 9 GHz in the direct acquisition way. On the digital receiver, the analog-to-digital converter (ADC) with 14-bit, two input channels and 3 Giga Samples per second (Gsps) are used to acquire observed signal, and the field-programmable-gate-array chip XCKU115 acts as the processing module. The new digital receiver can be used to directly sample the solar radio signals of frequency under 9 GHz. When receiving the solar radio signal above 9 GHz, the new digital receiver can save 1–2 stages of frequency down-conversion, and effectively improve many indexes of the solar radio observation system, i.e., the time resolution, analog front-end circuit, weight and volume of the analog circuit system. Compared with the digital receiver with sampling rate below 1 Gsps used in existing solar radio telescope, the new digital receiver reduces the frequency switching times of large bandwidth, which is beneficial to improving the frequency and time resolutions. The ADC sampling resolution of 14 bits, providing a large dynamic range, is very beneficial to observing smaller solar eruptions. This receiver, which would be used in the solar radio observation system, well meets the latest requirements with the resolutions of time (≤1 ms) and frequency (≤0.5 MHz) for fine observation of radio signals.

Solar radio bursts observations by Egypt- Alexandria CALLISTO spectrometer: First results

The newly installed CALLISTO spectrometer, hosted by the Department of Space Environment, Institute of Basic and Applied Sciences- EJUST, commenced operation on August 14, 2021. The system contains a cross dipole long-wavelength array antenna with high sensitivity to monitor solar radio transients. Its antenna was strategically positioned and appeared to be in the center of the CALLISTO network of spectrometers. Moreover, in the northern section of Africa, the Egypt-Alexandria CALLISTO and ALGERIA-CRAAG stations are the only ones operating. There are no stations in the West African region, while stations in the eastern part of Africa are not working. Thus, Egypt- Alexandria station serves as a reference for other stations within the e-CALLISTO network. Despite the low solar activity, the instrument detected several solar radio bursts not limited to type II, type III, and type V. A vigorous case study was conducted on two selected radio burst events to validate the authenticity of the recorded events. Other solar radio stations at different geographical locations recorded all the radio bursts detected by the spectrometer. The case study included brief analyses that indicated a type II radio burst observed on October 09, 2021, between 06:30 and 07:00 UT, was associated with an M1.6 solar flare located at N18E08 within NOAA-AR 2882 and a CME with a shock front speed of ∼ 978 km/s. However, the type III radio burst is neither CME nor solar flare associated. These analyses examine the instrument's capacity to provide real-time solar radio transient data 24 h a day to mitigate the challenges of data gaps faced in the African continent. Hence, the instrument has become an integral part of space weather monitoring and forecasting over the region and other parts of the globe.

A New Multichannel Parallel Real-time FFT Algorithm for a Solar Radio Observation System Based on FPGA

The real-time fast Fourier transform (FFT) is the essential algorithm for signal processing in a solar radio receiver. However, field-programmable gate array (FPGA) computation resources have become the limitation of real-time processing of signals with increasing time and spectral resolutions. It is necessary to design a real-time parallel FFT algorithm with reduced resource occupation in the development of future receiving systems. In this paper, we developed a multichannel parallel FFT algorithm named the multichannel parallel real-time fast Fourier transform (MPR-FFT), which can greatly reduce FPGA resource occupation while increasing the real-time processing speed. In this algorithm, the 4L simultaneous N-point FFTs are first converted into L simultaneous 4N-point FFTs. Fusion processing is then performed to obtain the 4 ∗ L ∗ N-point spectrum. This method has been used in developing a solar radio spectrometer, which works in the frequency range of 0.5–15 GHz in the Chashan Observatory. In this spectrometer, 16 channel MPR-FFT with 8k-point data is realized in a Xilinx UltraScale KU115 FPGA. The MPR-FFT algorithm reduced the computational resources to a large extent compared to the Cooley-Tukey-based parallel FFT method; for instance, the Look-Up-Table, Look-Up-Table RAM, Flip-Flop, and Digital Signal Process slices were reduced by 37%, 50%, 17%, and 2.48%, respectively. Although the MPR-FFT consumes 14 block RAM resources more than the Cooley-Tukey-based parallel FFT, the MPR-FFT algorithm presents an overall reduction in resource usage.

A deep learning method for the recognition of solar radio burst spectrum.

Solar radiation is the excitation source that affects the weather in the atmosphere of the earth, and some solar activities such as flares and coronal mass ejections are often accompanied by radio bursts. The spectrum of solar radio bursts is helpful for astronomers to explore the mechanism of radio bursts. With the development and progress of solar radio spectrum observation methods, the observation of the Sun can be done at almost all times of day. How to quickly and automatically identify the small proportion of burst data from the huge corpus of observation data has become an important research direction. The innovation of this study is to enhance the original radio spectrum dataset with unbalanced sample distribution, and a neural network model for solar radio spectrum image classification is proposed on this basis. This hybrid structure of joint convolution and a memory unit overcomes the shortcoming of the traditional convolution or memory model, which can only extract one-sided features of an image. By extracting the frequency structure features and time-series features at the same time, the sensitivity to the small features of the spectrum image can be enhanced. Based on the data of the Solar Broadband Radio Spectrometer (SBRS) in China, the proposed network model can improve the average classification accuracy of the spectrum image to 98.73%, which will be helpful for related astronomical research.