优先级 70 · 太阳耀斑
Automated Solar Radio Burst Detection Using Deep Learning on Augmented e-Callisto Data
基于增强e-Callisto数据的深度学习太阳射电暴自动检测
Vincenzo Timmel (Institute for Data Science, FHNW, Windisch, Switzerland), André Csillaghy (Institute for Data Science, FHNW, Windisch, Switzerland), Christian Monstein (IRSOL, Faculty of Informatics, USI, Locarno, Switzerland)
原文摘要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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AI 综述
AI-generated · 以原文为准
亮点首次将语音处理中的SpecAugment和TimeWarp用于太阳射电谱图增强,显著提升模型对未知仪器和低信噪比的鲁棒性;在同等精度下召回率(73.15%)超越日常人工目录(63%)。
脉络与展望早期太阳射电暴检测依赖信号处理算法,如Lobzin+ 2009基于Radon变换的自动识别系统,但需手动调参且泛化性不足。随后Carley+ 2020等引入监督机器学习,仍依赖手工特征。深度学习兴起后,Gordo+ 2023的deARCE和He+ 2023的MobileViT-SSDLite模型显著提升了检测性能,但多在有限仪器上训练。本研究将成熟的数据增强技术首次应用于太阳射电领域,有效缓解了硬件异构和低信噪比导致的泛化难题。展望未来,FlareSense可扩展至实时多仪器融合、爆发类型分类,并利用海量未标注数据,进一步推动空间天气预警的自动化。
预印本 2026-07-28 · 收录 2026-07-29