Solar flares are energetic events in the solar atmosphere that are often linked with solar radio bursts (SRBs). SRBs are observed at metric to decametric wavelengths and are classified into five spectral classes (Type I--V) based on their signature in dynamic spectra. The automatic detection and classification of SRBs is a challenge due to their heterogeneous form. Near-realtime detection and classification of SRBs has become a necessity in recent years due to large data rates generated by advanced radio telescopes such as the LOw Frequency ARray (LOFAR). In this study, we implement congruent deep learning models to automatically detect and classify Type III SRBs. We generated simulated Type III SRBs, which were comparable to Type IIIs seen in real observations, using a deep learning method known as Generative Adversarial Network (GAN). This simulated data was combined with observations from LOFAR to produce a training set that was used to train an object detection model known as YOLOv2 (You Only Look Once). Using this congruent deep learning model system, we can accurately detect Type III SRBs at a mean Average Precision (mAP) value of 77.71%.

翻译：太阳耀斑是太阳大气中的高能事件，常与太阳射电暴（SRBs）相关联。SRB在米波至十米波波长范围内被观测到，并根据其动态频谱特征分为五个光谱类别（I型至V型）。由于SRB形态具有异质性，其自动检测与分类是一项挑战。近年来，随着低频阵列（LOFAR）等先进射电望远镜产生海量数据，近实时检测与分类SRB已成为必要。本研究采用一致深度学习模型自动检测和分类III型SRB。我们使用生成对抗网络（GAN）这一深度学习方法，生成了与实际观测中III型SRB相似的模拟数据。将模拟数据与LOFAR观测数据结合，构建训练集，用于训练目标检测模型YOLOv2（仅看一次）。利用这一一致深度学习模型系统，我们能够以平均精度（mAP）77.71%的水平准确检测III型SRB。