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Solar Radio Bursts and Space Weather

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arxiv 2405.00959 v1 pith:275DTJRV submitted 2024-05-02 astro-ph.SR physics.space-ph

classification astro-ph.SRphysics.space-ph
keywords solarradioatmospherespaceweatherburstearthevents
verification ladder T0 review T1 audit T2 compute T3 formal
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Space Weather is the study of the conditions in the solar wind that can affect life on the surface of the Earth, particularly the increasingly technologically sophisticated devices that are part of modern life. Solar radio observations are relevant to such phenomena because they generally originate as events in the solar atmosphere, including flares, coronal mass ejections and shocks, that produce electromagnetic and particle radiations that impact the Earth. Low frequency solar radio emission arises in the solar atmosphere at the levels where these events occur: we can use frequency as a direct measure of density, and an indirect measure of height, in the atmosphere. The main radio burst types are described and illustrated using data from the Green Bank Solar Radio Burst Spectrometer, and their potential use as diagnostics of Space Weather is discussed.

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Cited by 3 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. OpenAlex reports about 43 citations worldwide. Full citation record

  1. Continuous Ultra-Low-Frequency Solar Radio Monitoring with ALBATROS from the High Arctic

    astro-ph.IM 2026-07 conditional novelty 6.0 of 10

    ALBATROS, an eight-station Arctic radio array, detected solar radio bursts across 1–125 MHz, saw the same bursts at all stations, and found them correlated with GOES soft X-ray flares.

  2. e-CALLISTO FITS Analyzer: A Software Framework for CALLISTO Solar Radio Data

    astro-ph.SR 2026-03 conditional novelty 6.0 of 10

    An open-source GUI that merges, cleans, and analyzes e-CALLISTO solar radio spectra to measure Type II burst drift rates and Newkirk-model shock speeds.

  3. Automated Solar Radio Burst Detection Using Deep Learning on Augmented e-Callisto Data

    astro-ph.SR 2026-07 conditional novelty 5.0 of 10

    FlareSense, a ResNet detector trained on 304,750 e-Callisto spectrograms with SpecAugment and TimeWarp, reaches 93% precision and 73.15% recall, outperforming routine expert cataloging at matched precision.

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