Identification and Extraction of Solar Radio Spikes Based on Deep Learning

Hou, Yongchun; Zhang, Q. M.; Feng, Shiwei; Du, Qingfu; Gao, Changqing; Zhao, Yao; Miao, Qiang

doi:10.1007/s11207-020-01718-9

Cited by 12 publications

(4 citation statements)

References 12 publications

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“…using algorithms based on deep belief networks, deep neural networks (multimodal or otherwise), convolutional neural networks, etc. For more information, see Ongsulee, 2017, Zhang et al, 2020, Moujahid, (last access: 2022.…”

Section: Introductionmentioning

confidence: 99%

Automatic Burst Detection in Solar Radio Spectrograms using Deep Learning: deARCE method

Gordo

Hernández

Mateo

et al. 2023

Preprint

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We present in detail an automatic radio burst detection system, based on the AlexNet convolutional neural network, for use with any kind of solar spectrograms. A full methodology for model training, performance evaluation and feedback to the model generator has been developed with special emphasis on: i) robustness tests against stochastic and overfit effects; ii) specific metrics adapted to the imbalanced nature of the solar burst scenario; iii) tunable parameters for probability threshold optimization; iv) burst coincidence cross match among e-Callisto stations and with external observatories (NOAA-SWPC). The resulting neural network configuration has been designed to accept data from observatories other than e-Callisto --either ground- or satellite-based. Typical False Negative and False Positive Scores in single-observatory mode are, respectively, in the 10--16\% and 6--8\% ranges, which improve further in cross-match mode. This mode includes new services (deARCE, Xmatch) allowing the end scientist to check at a glance if a solar radio burst has taken place with a high level of confidence.

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Section: Introductionmentioning

confidence: 99%

Automatic Burst Detection in Solar Radio Spectrograms using Deep Learning: deARCE method

Gordo

Hernández

Mateo

et al. 2023

Preprint

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“…Abduallah et al, 2022). Recently, Hou et al (2020) identiőed and extracted the solar radio spikes using the method of Faster Region-based Convolutional Neutral Network (Faster R-CNN) successfully, giving the AP value is 91%.…”

Section: Introductionmentioning

confidence: 99%

Automated detection and statistical study of solar radio spikes

Hou

Feng

et al. 2023

Astrophys Space Sci

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The most typical observational features of solar radio spikes are their short duration and narrow bandwidth. For these characteristics, we have improved the YOLOv5s network model, by adding inclined bounding frames, and attention and feature fusion mechanism modules. The decimeter-and meterwavelength spikes observed by the Solar Broad-band Radio Spectrometer in Huairou and the spectrograph in the Chashan Solar Radio Observatory 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 the durations, bandwidths, relative bandwidths, and frequency-drift rates. The spikes at decimeter and meter wavelengths are categorized again based on their frequency-drift rates, such as, positive, negative, and no measurable frequency-drift rates, respectively. We have carried out a statistical study on these categorized spikes. An interesting őnding is that the distribution of relative bandwidths at decimeter wavelengths is changed after the classiőcation. The distribution is asymmetric without the classiőcation. However, after the classiőcation the positive, negative, and no measurable frequency-drift spikes show a symmetrical distribution. These statistical results and őndings provide constraints on the formation of solar radio spikes.

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“…Recent research has turned to object detection algorithms arXiv:2105.13387v1 [astro-ph.SR] 27 May 2021 such as Faster R-CNN to identify solar radio bursts [7]. This deep learning neural network proved to be accurate at extracting small features of solar radio bursts with an average precision (AP) of 91% however, it doesn't have the performance of real time detection.…”

Section: Introductionmentioning

confidence: 99%

Type III solar radio burst detection: A deep learning approach

Scully

Flynn

Carley

et al. 2021

2021 32nd Irish Signals and Systems Conference (ISSC)

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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 methodology 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).

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Identification and Extraction of Solar Radio Spikes Based on Deep Learning

Cited by 12 publications

References 12 publications

Automatic Burst Detection in Solar Radio Spectrograms using Deep Learning: deARCE method

Automatic Burst Detection in Solar Radio Spectrograms using Deep Learning: deARCE method

Automated detection and statistical study of solar radio spikes

Type III solar radio burst detection: A deep learning approach

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