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.
Deep active learning–based classification of solar radio spectrogram data.The Astrophysical Journal Supplement Series, 279(1):25, 2025
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Automated Solar Radio Burst Detection Using Deep Learning on Augmented e-Callisto Data
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.