论文标题
使用无监督的机器学习在AIA/SDO图像上识别冠状孔
Identification of Coronal Holes on AIA/SDO images using unsupervised Machine Learning
论文作者
论文摘要
通过其磁性活动，太阳控制着地球附近的条件，从而产生了太空天气事件，这对我们的空间和地面技术产生了巨大影响。创造空间天气的最重要的太阳能磁特征之一是太阳风，它源自冠状孔（CHS）。因此，在太阳上鉴定CHS是太阳风的源区之一，对于获得预测能力至关重要。在这项研究中，我们使用了一种无监督的机器学习方法，$ k $ -Means，将大气成像组件在{\ IT {\ IT太阳能动力学天文台}（AIA/SDO）上拍摄的太阳的Pass带图像（AIA/SDO）以171Å，193Å\，和211Å，以及不同的组合。我们的结果表明，像素$ k $ -Means的聚类以及系统的预处理和后处理步骤与CNNNS等复杂方法（例如CNNS）提供了兼容的结果。更重要的是，我们的研究表明，需要一个CH数据库，即观察者独立达成有关CH边界的共识。然后，当使用有监督的方法或仅评估模型的好处时，该数据库可以用作“地面真相”。
Through its magnetic activity, the Sun governs the conditions in Earth's vicinity, creating space weather events, which have drastic effects on our space- and ground-based technology. One of the most important solar magnetic features creating the space weather is the solar wind, that originates from the coronal holes (CHs). The identification of the CHs on the Sun as one of the source regions of the solar wind is therefore crucial to achieve predictive capabilities. In this study, we used an unsupervised machine learning method, $k$-means, to pixel-wise cluster the passband images of the Sun taken by the Atmospheric Imaging Assembly on {\it the Solar Dynamics Observatory} (AIA/SDO) in 171 Å, 193 Å\,, and 211 Å\,in different combinations. Our results show that the pixel-wise $k$-means clustering together with systematic pre- and post-processing steps provides compatible results with those from complex methods, such as CNNs. More importantly, our study shows that there is a need for a CH database that a consensus about the CH boundaries are reached by observers independently. This database then can be used as the "ground truth", when using a supervised method or just to evaluate the goodness of the models.
