Solar flare catalog based on SDO/AIA EUV images: Composition and correlation with GOES/XRS X-ray flare magnitudes
Solar flare catalog based on SDO/AIA EUV images: Composition and correlation with GOES/XRS X-ray flare magnitudes
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DOI：
10.3389/fspas.2022.1031211
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发表时间：
2022-11
期刊：
影响因子：
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通讯作者：
Kiera van der Sande;N. Flyer;T. Berger;Riana Gagnon
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文献类型：
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作者：
Kiera van der Sande;N. Flyer;T. Berger;Riana Gagnon
用于太阳耀斑预测的监督机器学习（ML）模型依赖于给定输入数据集的准确标签，这些数据集通常来自GOES/XRS x射线耀斑目录。随着人们对利用紫外线（UV）和极紫外线（EUV）图像数据作为这些模型的输入越来越感兴趣，我们试图了解是否可以单独使用EUV数据来定义和量化耀斑活动。这将使我们能够摆脱GOES对耀斑的单像素测量定义，并使用我们用于耀斑预测的相同数据来创建标签。在这项工作中，我们提出了一个基于太阳动力学天文台（SDO）大气成像组件（AIA）的耀斑目录，涵盖了2010年至2017年GOES X射线等C， M和X的耀斑。我们使用全磁盘AIA图像的活动区域（AR）切割来匹配相应的SDO/日震和磁成像仪（HMI） SHARPS（空间天气HMI活动区域补丁），这些图像已广泛用于ML耀斑预测研究，从而允许标记AR数量以及耀斑的大小和时间。耀斑的开始、峰值和结束时间是使用对AIA时间序列数据的峰值查找算法来定义的，这些数据是通过对AIA切割点的强度求和获得的。采用极端随机树（ERT）回归模型将SDO/AIA耀斑星等映射到GOES x射线星等，实现了低方差回归。我们发现，在我们得到的AIA耀斑目录和GOES耀斑目录之间，85%的M/X耀斑精确重叠。然而，我们也发现了一些未在GOES目录中记录或错误标记的大型耀斑。
Supervised Machine Learning (ML) models for solar flare prediction rely on accurate labels for a given input data set, commonly obtained from the GOES/XRS X-ray flare catalog. With increasing interest in utilizing ultraviolet (UV) and extreme ultraviolet (EUV) image data as input to these models, we seek to understand if flaring activity can be defined and quantified using EUV data alone. This would allow us to move away from the GOES single pixel measurement definition of flares and use the same data we use for flare prediction for label creation. In this work, we present a Solar Dynamics Observatory (SDO) Atmospheric Imaging Assembly (AIA)-based flare catalog covering flare of GOES X-ray magnitudes C, M and X from 2010 to 2017. We use active region (AR) cutouts of full disk AIA images to match the corresponding SDO/Helioseismic and Magnetic Imager (HMI) SHARPS (Space weather HMI Active Region Patches) that have been extensively used in ML flare prediction studies, thus allowing for labeling of AR number as well as flare magnitude and timing. Flare start, peak, and end times are defined using a peak-finding algorithm on AIA time series data obtained by summing the intensity across the AIA cutouts. An extremely randomized trees (ERT) regression model is used to map SDO/AIA flare magnitudes to GOES X-ray magnitude, achieving a low-variance regression. We find an accurate overlap on 85% of M/X flares between our resulting AIA catalog and the GOES flare catalog. However, we also discover a number of large flares unrecorded or mislabeled in the GOES catalog.