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A Multi-wavelength Analysis of Active Regions and Sunspots by Comparison of Automatic Detection Algorithms

机译:通过比较自动检测算法对活动区域和黑子进行多波长分析

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Since the Solar Dynamics Observatory (SDO) began recording ≈ 1 TB of data per day, there has been an increased need to automatically extract features and events for further analysis. Here we compare the overall detection performance, correlations between extracted properties, and usability for feature tracking of four solar feature-detection algorithms: the Solar Monitor Active Region Tracker (SMART) detects active regions in line-of-sight magnetograms; the Automated Solar Activity Prediction code (ASAP) detects sunspots and pores in white-light continuum images; the Sunspot Tracking And Recognition Algorithm (STARA) detects sunspots in white-light continuum images; the Spatial Possibilistic Clustering Algorithm (SPoCA) automatically segments solar EUV images into active regions (AR), coronal holes (CH), and quiet Sun (QS). One month of data from the Solar and Heliospheric Observatory (SOHO)/Michelson Doppler Imager (MDI) and SOHO/Extreme Ultraviolet Imaging Telescope (EIT) instruments during 12 May – 23 June 2003 is analysed. The overall detection performance of each algorithm is benchmarked against National Oceanic and Atmospheric Administration (NOAA) and Solar Influences Data Analysis Center (SIDC) catalogues using various feature properties such as total sunspot area, which shows good agreement, and the number of features detected, which shows poor agreement. Principal Component Analysis indicates a clear distinction between photospheric properties, which are highly correlated to the first component and account for 52.86% of variability in the data set, and coronal properties, which are moderately correlated to both the first and second principal components. Finally, case studies of NOAA 10377 and 10365 are conducted to determine algorithm stability for tracking the evolution of individual features. We find that magnetic flux and total sunspot area are the best indicators of active-region emergence. Additionally, for NOAA 10365, it is shown that the onset of flaring occurs during both periods of magnetic-flux emergence and complexity development.
机译:自从太阳动力学天文台(SDO)开始每天记录≈1TB的数据以来,对自动提取要素和事件以进行进一步分析的需求日益增加。在这里,我们比较了四种太阳能特征检测算法的整体检测性能,提取的属性之间的相关性以及用于特征跟踪的可用性:太阳能监测器活动区域跟踪器(SMART)在视线磁图中检测活动区域;自动太阳活动预测代码(ASAP)可检测白光连续图像中的黑子和毛孔;太阳黑子跟踪与识别算法(STARA)可以检测白光连续图像中的太阳黑子;空间可能性聚类算法(SPoCA)会将太阳EUV图像自动分割为活动区域(AR),日冕孔(CH)和安静的太阳(QS)。分析了2003年5月12日至6月23日来自太阳和日球天文台(SOHO)/米歇尔森多普勒成像仪(MDI)和SOHO /极端紫外成像望远镜(EIT)仪器的一个月数据。每种算法的整体检测性能均以美国国家海洋和大气管理局(NOAA)和太阳影响数据分析中心(SIDC)目录为基准,并使用各种特征属性(例如总黑子面积)显示出良好的一致性,并且所检测到的特征数量也很多,这表明协议不力。主成分分析表明,与第一成分高度相关且占数据集变异性52.86%的光球特性与与第一和第二主成分均中等相关的日冕特性之间存在明显的区别。最后,进行了NOAA 10377和10365的案例研究,以确定用于跟踪单个特征演变的算法稳定性。我们发现,磁通量和太阳黑子总面积是活动区域出现的最好指标。此外,对于NOAA 10365,表明在磁通量出现和复杂性发展的两个阶段都发生了扩口。

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