We present a concept for a machine-learning classification of hard X-ray (HXR) emissions from solar flares observed by the Reuven Ramaty High Energy Solar Spectroscopic Imager (RHESSI), identifying flares that are either occulted by the solar limb or located on the solar disk. Although HXR observations of occulted flares are important for particle-acceleration studies, HXR data analyses for past observations were time consuming and required specialized expertise. Machine-learning techniques are promising for this situation, and we constructed a sample model to demonstrate the concept using a deep-learning technique. Input data to the model are HXR spectrograms that are easily produced from RHESSI data. The model can detect occulted flares without the need for image reconstruction nor for visual inspection by experts. A technique of convolutional neural networks was used in this model by regarding the input data as images. Our model achieved a classification accuracy better than 90%, and the ability for the application of the method to either event screening or for an event alert for occulted flares was successfully demonstrated.

我们提出了一种概念的机器学习方法，该方法是通过鲁汶拉玛特高能太阳能光谱成像仪观察到的来自太阳耀斑的硬X射线（HXR）排放的机器学习分类的（RHESSI），识别出被太阳肢掩盖或位于太阳盘上的耀斑。尽管隐匿耀斑的HXR观测对于粒子加速研究非常重要，但过去观测的HXR数据分析非常耗时，需要专门知识。机器学习技术在这种情况下很有希望，我们构建了一个样本模型来使用深度学习技术来演示该概念。模型的输入数据是可以从RHESSI数据轻松生成的HXR频谱图。该模型可以检测隐匿的耀斑，而无需图像重建或专家进行目视检查。通过将输入数据视为图像，在该模型中使用了卷积神经网络技术。我们的模型实现了超过90％的分类精度，