AI 中文总结
研究利用深度学习在增强的电子卡利斯托数据上进行自动太阳射电暴检测，解决现有检测依赖人工、受硬件和信噪比限制的问题，为太阳射电暴监测提供新方法以实现更好预警和研究。
AI 中文摘要
太阳射电暴是与太阳耀斑和日冕物质抛射相关的高能事件的特征，会干扰地面和天基通信系统。实时自动暴监测能在相关粒子到达地球前数十分至数小时发出预警并为长期统计研究提供基础。电子卡利斯托网络是全球太阳射电光谱仪系统，目前暴检测和标记很大程度依赖人类专家，因硬件异质性和低信噪比限制了可扩展性和实时适用性。
英文摘要
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.
