Solar Radio Spectrum Classification Based on ConvLSTM

In solar radio observation, it is inevitable to be disturbed by noise, and the solar radio spectrum is affected. The solar radio spectrum with good quality can provide useful information in analyzing solar activities and improve the accuracy of automatic detection and classification of solar radio bursts. A noise reduction method for solar radio spectrum based on improved guided filter and morphological cascade is proposed in this paper. Firstly, the background noise of solar radio spectrum is subtracted, and the cross-fringe interference caused by channel effect is reduced. Secondly, the improved guided filtering is used to keep the edge of the solar radio burst as far as possible when filtering the burst region of the spectrum. Finally, more noise from other external factors is removed by morphological operation. The experimental results show that the proposed method is effective in removing noise from solar radio spectrum.

Multimodal deep learning for solar radio burst classification

This study use deep learning to automatically detect the burst information in the solar radio spectrum, which can automatically identify burst type III in the spectrum and mark the bounding box at the corresponding position, giving characteristic parameters such as the burst duration, frequency bandwidth, start and stop frequency, start and stop time, center frequency, frequency drift rate and shape profile.

With the development of solar radio spectrometer, a large number of observational data has been obtained and the manual detection is difficult to reach the research needs. An automatic detection method of solar radio burst using kmeans clustering was presented in this paper. K-means clustering is introduced to classify the burst points in solar radio spectrum, and it can do better in high spectral and time resolution spectrometer. The experimental results show that the proposed method is effective.

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.

During the intense solar radio bursts on 6 September 2017, Global Navigation Satellite Systems (GNSS) signal interferences were observed at ground stations in the European longitude sector from 20°N to 70°N for all GNSS satellites in view including GPS, GLONASS, and Galileo. The solar radio noise reduced the signal‐to‐noise ratio with clear frequency dependence. The impact of the radio burst has been found at L2 and L5 frequencies, but not at L1 frequency. The ground observation of the solar radio spectrum between 1.0 and 2.0 GHz corresponds well to such frequency dependence. The maximum signal‐to‐noise ratio reduction of ‐10 dB was found when the solar radio flux was pulsating around 2,000 solar flux unit level. Precise point positioning results show that accuracy is reduced with stronger deviation for dual‐frequency solutions than for single‐frequency solutions based on L1 signal only. The positioning error refers rather to the solar extreme ultraviolet flare than to solar radio interferences. The results presented here are a clear indication of frequency‐dependent GNSS performance degradation during strong space weather events.

Solar radio bursts (SRBs) are extreme space weather events characterized by intense solar radio emissions that are closely related to solar flares. They represent signatures of the same underlying processes that are responsible for well-documented solar phenomena such as sunspots, solar flares, and coronal mass ejections (CMEs). The study of SRBs holds significant importance as it provides a means to monitor and predict solar flares and CMEs, enhancing our ability to forecast potential impacts on Earth’s communications and satellites. Typically, SRBs below several hundred megahertz can be categorized into five types (I–V), with type II and type III bursts being the most prevalent. This study introduces a novel approach based on the YOLOv7 model for the detection and classification of type II and type III SRBs. The proposed method effectively identifies and classifies various SRB types, achieving a mean average precision accuracy of 73.5%. A trained neural network was deployed for SRB detection in the Chashan Broadband Solar radio spectrograph at meter wavelength (CBSm) data, enabling the extraction of valuable SRB information for subsequent research. This demonstrates that even when we are dealing with extensive datasets, this method can automatically recognize outbursts and extract pertinent physical information. Although our experiments with the CBSm dataset currently rely on the daily spectrum, further advancements in CBSm backend data processing techniques are expected to enable near-real-time burst detection, which is a powerful tool for accurately assessing and analyzing SRBs, and significantly contribute to the field of space weather forecasting and protective measures. Furthermore, the applicability of this method to other stations within the Chinese Meridian Project II (e.g., Mingantu Spectral Radioheliograph and Daocheng Solar Radio Telescope) enhances the capability of space weather data fusion and model development. Therefore, this research represents a substantial contribution to the domain of space weather research, offering a valuable tool for the detection and classification of SRBs and thereby improving our ability to predict and mitigate the impacts of extreme space weather events on Earth’s technology and infrastructure.

Context. Solar radio bursts originate mainly from high energy electrons accelerated in solar eruptions like solar flares, jets, and coronal mass ejections. A sub-category of solar radio bursts with short time duration may be used as a proxy to understand wave generation and propagation within the corona. Aims. Complete case studies of the source size, position, and kinematics of short term bursts are very rare due to instrumental limitations. A comprehensive multi-frequency spectroscopic and imaging study was carried out of a clear example of a solar type IIIb-III pair. Methods. In this work, the source of the radio burst was imaged with the interferometric mode, using the remote baselines of the LOw Frequency ARray (LOFAR). A detailed analysis of the fine structures in the spectrum and of the radio source motion with imaging was conducted. Results. The study shows how the fundamental and harmonic components have a significantly different source motion. The apparent source of the fundamental emission at 26.56 MHz displaces away from the solar disk center at about four times the speed of light, while the apparent source of the harmonic emission at the same frequency shows a speed of < 0.02 c. The source size of the harmonic emission observed in this case is smaller than that in previous studies, indicating the importance of the use of remote baselines.

With the development of solar radio spectrometer, it is difficult to process a large number of observed data quickly by manual detection method. Yunnan astronomical observatories (YNAO) have two solar radio spectrometers with high time and frequency resolution. An automatic detection method of solar radio burst for decimetric and metric data of YNAO is proposed in this paper. The duration of solar radio burst was counted and analyzed. Channel normalization was used to denoise the original solar radio image. Through experimental comparison, Otsu method was selected as a binary method of solar radio spectrum, and open and close operations were used to smooth the binary image. Experiments show that the proposed method for automatic detection of solar radio bursts is effective.

Abstract. This article summarises the results of an analysis of solar radio bursts (SRBs) detected by the Compound Astronomical Low-cost Low-frequency Instrument for Spectroscopy and Transportable Observatory (CALLISTO) spectrometer hosted by the University of Rwanda. The data analysed were detected during the first year (2014–2015) of the instrument operation. Using quick plots provided by the e-CALLISTO website, a total of 201 intense and well-separated solar radio bursts detected by the CALLISTO station located in Rwanda, are found consisting of 4 type II, 175 type III and 22 type IV radio bursts. It is found that all analysed type II and ∼ 37 % of type III bursts are associated with impulsive solar flares, while the minority (∼ 13 %) of type IV radio bursts are associated with solar flares. Furthermore, all type II radio bursts are associated with coronal mass ejections (CMEs), ∼ 44 % of type III bursts are associated with CMEs, and the majority (∼ 82 %) of type IV bursts were accompanied by CMEs. With aid of the atmospheric imaging assembly (AIA) images on board the Solar Dynamics Observatory (SDO), the location of open magnetic field lines of non-flare-associated type III radio bursts are shown. The same images are used to show the magnetic loops in the solar corona for type IV radio bursts observed in the absence of solar flares and/or CMEs. Findings from this study indicate that analysis of SRBs that are observed from the ground can provide a significant contribution to the early diagnosis of solar transients phenomena, such as solar flares and CMEs, which are major drivers of potential space weather hazards.

view Abstract Citations (21) References (7) Co-Reads Similar Papers Volume Content Graphics Metrics Export Citation NASA/ADS Direct Radiation from a Strong DC Electric Field Wentzel, Donat G. Abstract Simulations have shown that a strong dc electric field yields, after electrostatic transients, a beam of electrons that is nearly flat in velocity distribution yet emits strong electrostatic and electromagnetic waves. Electric energy is converted 'directly' into radiation. This radiation is derived analytically in order to learn how the radiation may depend on field strength and beam density in astronomical objects. Any wave with a significant electric component along the dc electric field grows exponentially in time, with the growth rate proportional to the number of electrons in the beam. The lower limits on the dc electric field and the beam density are set by competing plasma processes. The results are related to solar millisecond radio bursts. Publication: The Astrophysical Journal Pub Date: May 1991 DOI: 10.1086/170049 Bibcode: 1991ApJ...373..285W Keywords: Direct Current; Electric Fields; Plasma Radiation; Solar Flares; Solar Radio Bursts; Analytic Functions; Electromagnetic Radiation; Electron Beams; Electrostatic Waves; Velocity Distribution; Solar Physics; PLASMAS; RADIATION MECHANISMS; SUN: FLARES; SUN: RADIO RADIATION full text sources ADS |

Solar Radio Bursts (SRBs) are generally observed in dynamic spectra and have five major spectral classes, labelled Type I to Type V depending on their shape and extent in frequency and time. Due to their complex characterization, a challenge in solar radio physics is the automatic detection and classification of such radio bursts. Classification of SRBs has become fundamental in recent years due to large data rates generated by advanced radio telescopes such as the LOw-Frequency ARray, (LOFAR). Current state-of-the-art algorithms implement the Hough or Radon transform as a means of detecting predefined parametric shapes in images. These algorithms achieve up to 84% accuracy, depending on the Type of radio burst being classified. Other techniques include procedures that rely on Constant-False-Alarm-Rate detection, which is essentially detection of radio bursts using a de-noising and adaptive threshold in dynamic spectra. It works well for a variety of different Types of radio bursts and achieves an accuracy of up to 70%. In this research, we are introducing a Convolutional Neural Network (CNN) named You Only Look Once v2 (YOLOv2) for solar radio burst classification. By using Type III simulation methods we can train the algorithm to classify real Type III solar radio bursts in real-time at an accuracy of 82.63% with a maximum 77 frames per second (fps).

B-factor is a measure of ray attenuation or scattering caused by atomic thermal motion during X-ray diffraction of protein crystal structure. B-factor reflects the vibration of atoms; hence, it is the most common experimental descriptor of protein flexibility and has been extensively applied in the studies of protein dynamics, screening of bioactive small molecules, and protein engineering. The prediction of B-factor profiles has considerable significance for analyzing the dynamic properties of unknown proteins. Deep learning technology has developed rapidly in recent years and has been widely implemented in many research fields, especially structural biology. In this paper, a deep neural network model based on bidirectional long short-term memory (biLSTM) network is proposed to predict the B-factor profile of a protein by combining its sequence-based features and structure-based features. Based on a large dataset of high-resolution proteins, our method predicts the B-factor profiles with an average Pearson correlation coefficient (PCC) of 0.71, and 85% of the B-factor profiles have a PCC greater than 0.6, which indicates a strong correlation between predicted and experimental values. In addition, our method remarkably outperforms the existing methods on four test datasets with different protein sizes.

We know Coronal Mass Ejections (CME) and flares are the most energetic phenomena happening on the Sun. Until now the information about origin and trigger mechanism of CMEs remains scarce. Also, there is unconclusive information about the association between them and flares although progress has been made in recent years. Multi-spectral observations suggested that the flare energy release occurs in regions from where the decimetric radio emission originates. In this case, investigations of the solar emission in this wavelength range can give us valuable information about these questions. During last solar maximum the Brazilian Solar Spectroscope (BSS) observed the solar radio spectrum (1–2.5 GHz) with high time (100–20 ms) and frequency (50–100 channels) resolutions on a daily (11–19 UT) basis. A survey during the period 1999–2002, shows that a significant fraction (20% –57 events) of CMEs recorded by LASCO has an association with the spectra of radio bursts recorded by BSS. Analysis of the radio spectrum associated to CME shows there is a dominance of continuum and/or pulsation and that the association becomes stronger when we consider the CME acceleration since its origin on the Sun. A statistics of this association between CME dynamics and the characteristics of decimetric radio bursts recorded by BSS is presented. Emphasis is given to observations of the association with CME start time.

This paper investigates the incidence of coherent emission in solar radio bursts, using a revised catalog of 3800 solar radio bursts observed by the Nobeyama Radio Polarimeters from 1988 to 2023. We focus on the 1.0 and 2.0 GHz data, where radio fluxes of order 1010 Jy have been observed. Previous work has suggested that these bursts are due to electron cyclotron maser (ECM) emission. In at least one well-studied case, the bright emission at 1 GHz consists of narrowband spikes of millisecond duration. Coherent emission at 1 GHz can be distinguished from traditional incoherent gyrosynchrotron flare emission based on the radio spectrum: Gyrosynchrotron emission at 1 GHz usually has a spectrum rising with frequency, so bursts in which 1 GHz is stronger than higher-frequency measurements are unlikely to be incoherent gyrosynchrotron. Based on this criterion, it is found that for bursts exceeding 100 sfu, three-quarters of all bursts at 1 GHz and half of all 2 GHz bursts have a dominant coherent emission component, assumed to be ECM. The majority of the very bright bursts at 1 GHz are highly circularly polarized, consistent with a coherent emission mechanism, but not always 100% polarized. The frequency range from 1 to 2 GHz is heavily utilized for terrestrial applications, and these results are relevant for understanding the extreme flux levels that may impact such applications. Further, they provide a reference for comparison with the study of ECM emission from other stars and potentially exoplanets.