Space weather study through analysis of solar radio bursts detected by a single-station CALLISTO spectrometer

Ndacyayisenga, Theogene; Umuhire, Ange Cynthia; Uwamahoro, Jean; Monstein, C.

doi:10.5194/angeo-39-945-2021

Cited by 3 publications

(2 citation statements)

References 82 publications

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“…The results from current study also indicate that analyzed SRBs were associate by ionospheric disturbances as demonstrated by TEC enhancement. The findings of this study demonstrate that it is possible to track well the progress of solar cycle 25 and forecast the intensity of associated space weather phenomena by analyzing and characterizing the physical features accompanying Type II SRBs observed from the ground (Ndacyayisenga et al, 2021a).…”

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confidence: 79%

“…Only 7 of the 31 type II radio bursts studied, on the other hand, are not a potential event to disrupt space weather because they are not associated with a CME or have an immediate space weather impact, whereas the rest can be used to track geomagnetic storms within the next five days from the onset of associated CMEs. Solar radio bursts are among the earliest observed signatures of erupting events in the solar corona (Salmane et al, 2018;Ndacyayisenga et al, 2021a).…”

Section: March 24 -April 3 2022mentioning

confidence: 99%

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An Overview of Solar Radio Type II Bursts through analysis of associated solar and near Earth space weather features during Ascending phase of SC 25

Ndacyayisenga

Uwamahoro

Rabiu

et al. 2023

Preprint

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Abstract. Type II solar radio bursts are the signatures of particle acceleration caused by shock waves in the solar atmosphere and interplanetary space. Being electromagnetic radiation that travel at the speed of light, they can serve as ground observed data to provide early notice of incoming solar storm disturbances. An observational overview of 31 Type II bursts which occurred in the period between May 2021 to December 2022 is made. We analyzed associated parameters such as bandwidth, drift rates, starting frequency to evaluate their dynamical parameters such as the shock and Alfvén speeds to estimate the Alfvén Mach number as well as the coronal magnetic field strength using Rankine-Hugoniot relation. We also evaluated accompanying space weather implication in terms of ionospheric total electron content (TEC) enhancement. At heliocentric distance ∼ 1−2 R⊙, the shock and the Alfvén speeds are in the range 504–1301 km s−1 and 368–837 km s−1, respectively. The Alfvén Mach number is of the order of 1.2 ≤ MA ≤ 1.8 at the same heliocentric distance, and the magnetic field strength shows excellent consistency and could be fit with a single power-law distribution of the type B(r) = 6.56 r −3.92 G. The study finds that 15/31 type II radio bursts are associated with some aspects of space weather such as radio blackouts and/or polar cap absorption events, that are the signature of solar proton enhancement and solar energetic particle events. Observed and analyzed Type II events correlated well with observed ionospheric storm indicated by the TEC enhancement. The findings from this study indicate that through analysis of type II SRBs observed from the ground and their physical features characteristics, it is possible to monitor the current progress of solar cycle 25 and predict the intensity of associated space weather phenomena.

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confidence: 79%

Section: March 24 -April 3 2022mentioning

confidence: 99%

An Overview of Solar Radio Type II Bursts through analysis of associated solar and near Earth space weather features during Ascending phase of SC 25

Ndacyayisenga

Uwamahoro

Rabiu

et al. 2023

Preprint

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Automatic Burst Detection in Solar Radio Spectrograms Using Deep Learning: deARCE Method

et al. 2023

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We present in detail an automatic radio-burst detection system, based on the convolutional neural network, for use with any kind of solar spectrogram. 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 overfitting effects, ii) specific metrics adapted to the unbalanced nature of the solar-burst scenario, iii) tunable parameters for probability-threshold optimization, and 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 spacecraft-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 (, ) allowing the end-user to check at a glance if a solar radio burst has taken place with a high level of confidence.

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Sunspot Group Detection and Classification by Dual Stream Convolutional Neural Network Method

Mkwanda,

Tian,

2024

Res. Astron. Astrophys.

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The automatic detection and analysis of sunspots play a crucial role in understanding solar dynamics and predicting space weather events. This paper proposes a novel method for sunspot group detection and classification called the Dual Stream Convolutional Neural Network with Attention Mechanism (DSCNN-AM) . The network consists of two parallel streams each processing different input data allowing for joint processing of spatial and temporal information while classifying sunspots. It takes in the white light images as well as the corresponding magnetic images that reveal both the optical and magnetic features of sunspots. The extracted features are then fused and processed by fully connected layers to perform detection and classification.The attention mechanism is further integrated to address the ``edge dimming" problem which improves the model's ability to handle sunspots near the edge of the solar disk. The network is trained and tested on the SOLAR-STORM1 dataset. The results demonstrate that the DSCNN with the attention mechanism achieves superior performance compared to existing methods, with a total accuracy exceeding 90%.Keywords:Sunspots, Mount Wilson, DSCNN, Deep Learning

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Space weather study through analysis of solar radio bursts detected by a single-station CALLISTO spectrometer

Cited by 3 publications

References 82 publications

An Overview of Solar Radio Type II Bursts through analysis of associated solar and near Earth space weather features during Ascending phase of SC 25

An Overview of Solar Radio Type II Bursts through analysis of associated solar and near Earth space weather features during Ascending phase of SC 25

Automatic Burst Detection in Solar Radio Spectrograms Using Deep Learning: deARCE Method

Sunspot Group Detection and Classification by Dual Stream Convolutional Neural Network Method

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