Solar energy paper index
Automated Solar Radio Burst Detection Using Deep Learning on Augmented e-Callisto Data
One-line summary
A solar energy research paper on Automated Solar Radio Burst Detection Using Deep Learning on Augmented e-Callisto Data.
Engineering notes
Engineering notes will be added by the Power for Solar editorial team.
Chinese explanation / 中文解读
中文解读待补充：本站会优先为光伏效率、钙钛矿太阳能电池、储能技术、太阳能热利用、BIPV、并网技术等高价值论文补充中文说明。
Original abstract
Solar radio bursts are signatures of energetic events associated with solar flares and coronal mass ejections and can interfere with terrestrial and space-based communication systems. Real-time automatic burst monitoring enables early warnings tens of minutes to hours before associated particles reach Earth and provides the basis for long-term statistical studies. The e-Callisto network is a worldwide system of solar radio spectrometers providing continuous observations, with its instruments collectively covering frequencies from approximately 20 MHz to 1 GHz. Burst detection and labeling currently rely largely on human experts, limiting scalability and real-time applicability due to hardware heterogeneity and low signal-to-noise ratios.
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