基于机器学习的日冕仪图像分类方法研究
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(1.中国科学院紫金山天文台南京210023;2.中国科学院暗物质和空间天文重点实验室南京210023;3.武汉理工大学计算机科学与技术学院武汉430070;4.中国科学技术大学天文与空间科学学院合肥230026;5.南京大学计算机软件新技术国家重点实验室南京210023

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Automatic Coronagraph Image Classification with Machine Learning Methods
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1. Purple Mountain Observatory, Chinese Academy of Sciences, Nanjing 210023;2. Key Laboratory of Dark Matter and Space Astronomy, Chinese Academy of Sciences, Nanjing 210023;3. School of Computer Science and Technology, Wuhan University of Technology, Wuhan 430070;4. School of Astronomy and Space Science, University of Science and Technology of China, Hefei 230026;5. State Key Laboratory for Novel Software Technology, Nanjing University, Nanjing 210023;

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    摘要:

    日冕物质抛射(Coronal Mass Ejection, CME)的检测是建立CME事件库和实现对CME在行星际传播的预报的重要前提. 通过Visual Geometry Group (VGG) 16卷积神经网络方法对日冕仪图像进行自动分类. 基于大角度光谱日冕仪(Large Angle and Spectrometric Coronagraph Experiment, LASCO) C2的白光日冕仪图像, 根据是否观测到CME对图像进行标记. 将标记分类的数据集用于VGG模型的训练, 该模型在测试集分类的准确率达到92.5%. 根据检测得到的标签结果, 结合时空连续性规则, 消除了误判区域, 有效分类出CME图像序列. 与Coordinated Data Analysis Workshops (CDAW)人工事件库比较, 分类出的CME图像序列能够较完整地包含CME事件, 且对弱CME结构有较高的检测灵敏度. 未来先进天基太阳天文台(Advanced Space-based Solar Observatory, ASO-S)卫星的莱曼阿尔法太阳望远镜将搭载有白光日冕仪(Solar Corona Imager, SCI), 使用此分类方法将该仪器产生的日冕图像按有无CME分类. 含CME标签的图像将推送给中国的各空间天气预报中心, 对CME进行预警.

    Abstract:

    The detection of Coronal Mass Ejections (CMEs) is an important prerequisite for establishing a CME event database and realizing the prediction of CME interplanetary propagation. The Lyman-alpha Solar Telescope (LST) aboard the ASO-S (Advanced Space-based Solar Observatory) satellite will be equipped with a white-light coronagraph. The images with CME detected will be distributed to various space weather prediction centers in China for CME early warning. To automatically and effectively classify coronagraph images, we applied the Visual Geometry Group (VGG) 16 convolutional neural network method. Firstly, based on the image of the white light coronagraph of Large Angle and Spectrometric Coronagraph Experiment (LASCO) C2, we labeled the images according to whether a CME is observed. Then, the data set was used for training the VGG model. We find that the accuracy of the model in the test set classification reaches 92.5%. Next, according to the obtained classification results and combined with the space-time continuity rules, we corrected the mislabeling of images, and derived our final CME image sequences. Compared with the manual CME catalog of Coordinated Data Analysis Workshops (CDAW), the classified CME image sequences can include CME data more completely, and have higher detection sensitivity for weak CME structures.

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单家辉,封莉,袁汉青,张岩,钟忺,甘为群,黎辉,黄宇.基于机器学习的日冕仪图像分类方法研究[J].天文学报,2020,(4):129-139.

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  • 收稿日期:2020-02-18
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  • 在线发布日期: 2020-07-30
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