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arXiv 2203.10491astro-ph.SRastro-ph.IM
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基于无监督机器学习的AIA/SDO图像冠状孔识别

Identification of Coronal Holes on AIA/SDO images using unsupervised Machine Learning

Fadil Inceoglu, Yuri Y. Shprits, Stephan G. Heinemann, Stefano Bianco

摘要:

通过其磁场活动,太阳控制着地球附近的条件,产生空间天气事件,对我们的空间和地面技术产生巨大影响。产生空间天气的最重要的太阳磁场特征之一是源自日冕洞(CH)的太阳风。因此,确定太阳上的CHs作为太阳风的源区之一,对于实现预测能力至关重要。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,以不同的组合对{ \it the Solar Dynamics Observatory}(AIA/SDO)上的大气成像组件在171毫秒,193毫秒和211毫秒拍摄的太阳通带图像进行像素聚类。我们的研究结果表明,逐像素$k$均值聚类与系统的预处理和后处理步骤一起提供了与复杂方法(如CNN)兼容的结果。更重要的是,我们的研究表明,有必要对CH数据库,一个共识的CH边界的观察员独立达成。然后,当使用监督方法或仅评估模型的优度时,该数据库可以用作“地面实况”。

英文摘要:

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.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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