Zahra Tajik, Nastaran Farhang, Hossein Safari and Michael S. Wheatland
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引用次数: 0
摘要
太阳和恒星磁斑(即从内部到达表面的磁通量)被认为是恒星大气状况的主要来源。在此,我们应用复杂网络方法,研究其在识别这些特征方面的功效。为此,我们使用了太阳动力学天文台(Solar Dynamics Observatory)上的太阳地震和磁成像仪(Helioseismic and Magnetic Imager)提供的视线磁图。我们根据极性相反的像素对之间的特定可见度图条件构建磁网络,并搜索这些区域之间可能存在的联系。复杂的网络有助于构建节点度和 PageRank 图像,将下坡算法应用于节点度图像可将像素分组为与磁图斑块一一对应的特征。这种方法有望识别大小磁图斑块的非复杂形态结构。我们观察到,随着时间的推移,节点度图像特征的变化与空间磁斑块有效对应。通过目测评估,我们估计识别小尺度特征(一或两个像素大小)的平均误差率约为 1%。
Complex Network View of the Sun’s Magnetic Patches. I. Identification
Solar and stellar magnetic patches (i.e., magnetic fluxes that reach the surface from the interior) are believed to be the primary sources of a star’s atmospheric conditions. Here, we apply the complex network approach and investigate its efficacy in the identification of these features. For this purpose, we use the line-of-sight magnetograms provided by the Helioseismic and Magnetic Imager on board the Solar Dynamics Observatory. We construct the magnetic network following a specific visibility graph condition between pairs of pixels with opposite polarities and search for possible links between these regions. The complex network facilitates the construction of node degrees and PageRank images, and applying the downhill algorithm to node-degree images allows for the grouping of pixels into features corresponding to one-to-one matches with magnetogram patches. This approach promisingly serves to identify the nontrivial morphological structure of the magnetic patches for small and large sizes. We observe that the changes in the features of the node-degree images effectively correspond to the cospatial magnetic patches over time. Through visual assessment, we estimate an average false-negative error rate of approximately 1% in identifying small-scale features (one or two pixels in size).