A Systematic Magnetic Polarity Inversion Line Data Set from SDO/HMI Magnetograms

被引:4
|
作者
Ji, Anli [1 ]
Cai, Xumin [1 ]
Khasayeva, Nigar [1 ]
Georgoulis, Manolis K. [2 ]
Martens, Petrus C. [3 ]
Angryk, Rafal A. [1 ]
Aydin, Berkay [1 ]
机构
[1] Georgia State Univ, Dept Comp Sci, Atlanta, GA 30302 USA
[2] Acad Athens, Res Ctr Astron & Appl Math, Athens, Greece
[3] Georgia State Univ, Dept Phys & Astron, Atlanta, GA USA
来源
基金
美国国家科学基金会;
关键词
ACTIVE REGIONS; SOLAR; FIELD; MODEL;
D O I
10.3847/1538-4365/acb43a
中图分类号
P1 [天文学];
学科分类号
0704 ;
摘要
Magnetic polarity inversion lines (PILs) detected in solar active regions have long been recognized as arguably the most essential feature for triggering instabilities such as flares and eruptive events (i.e., eruptive flares and coronal mass ejections). In recent years, efforts have been focused on using features engineered from PILs for solar eruption prediction. However, PIL rasters and metadata are often generated as by-products and are not accessible for public use, which limits their utilization in data-intensive space weather analytics applications. We introduce a large-scale publicly available PIL data set covering practically the entire solar cycle 24 for applying to various space weather forecasting and analytics tasks. The data set is created using both radial magnetic field (B_r) and line-of-sight (B_LoS) magnetograms from the Solar Dynamics Observatory's Helioseismic and Magnetic Imager Active Region Patches (HARP) that involve 4090 HARP series ranging from 2010 May to 2019 March. This data set includes three PIL-related binary masks of rasters: the actual PILs as per the spatial analysis of the magnetograms, the region of polarity inversion, and the convex hull of PILs, along with time-series-structured metadata extracted from these masks. We also provide a preliminary exploratory analysis of selected features aiming to correlate time series of feature metadata and eruptive activity originating from active regions. We envision that this comprehensive PIL data set will complement existing data sets used for space weather forecasting and benefit research in related areas, specifically in better understanding the PIL structure, evolution, and role in eruptions.
引用
收藏
页数:16
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