Automatic detection of solar events, especially uncommon events such as coronal dimming (CD) and coronal wave (CW), is very important in solar physics research. The CD and CW are not only related to the detection of coronal mass ejections (CMEs) but also affect space weather. In this paper, we have studied methods for automatically detecting them. In addition, we have collected and processed a dataset that includes the solar images and event records, where the solar images come from the Atmospheric Imaging Assembly (AIA) of Solar Dynamics Observatory (SDO) and the event records come from Heliophysics Event Knowledgebase (HEK). Different from the methods used before, we introduce the idea of deep learning. We train single-wavelength and multiwavelength models based on Faster R-CNN. In terms of accuracy, the single-wavelength model performs better. The multiwavelength model has a better detection performance on multiple solar events than the single-wavelength model.

中文翻译：

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使用更快的R-CNN检测冠状调光和冠状波的单波长和多波长

太阳事件的自动检测，特别是诸如日光变暗（CD）和日冕波（CW）等罕见事件，在太阳物理学研究中非常重要。CD和CW不仅与冠状物质抛射（CME）的检测有关，而且还影响太空天气。在本文中，我们研究了自动检测它们的方法。此外，我们还收集并处理了包含太阳图像和事件记录的数据集，其中太阳图像来自太阳动力学天文台（SDO）的大气成像组件（AIA），事件记录来自太阳物理事件知识库（HEK） ）。与以前使用的方法不同，我们介绍了深度学习的思想。我们基于Faster R-CNN训练单波长和多波长模型。在准确性方面，单波长模型表现更好。