TitleA Large-Scale Dataset of Three-Dimensional Solar Magnetic Fields Extrapolated by Nonlinear Force-Free Method
AuthorsZhao, Zhongrui
Xu, Long
Zhu, Xiaoshuai
Zhang, Xinze
Liu, Sixuan
Huang, Xin
Ren, Zhixiang
Tian, Yonghong
AffiliationChinese Acad Sci, Natl Space Sci Ctr, State Key Lab Space Weather, Beijing 100190, Peoples R China
Univ Chinese Acad Sci, Sch Astron & Space Sci, Beijing 100049, Peoples R China
Peng Cheng Lab, Shenzhen 518000, Peoples R China
Peking Univ, Sch Elect & Comp Engn, Shenzhen Grad Sch, Shenzhen 518055, Peoples R China
Issue Date30-Mar-2023
AbstractIt has been widely accepted that solar magnetic field manipulates all solar activities, especially violent solar bursts in solar corona. Thus, it is extremely important to reconstruct three-dimentional (3D) magnetic field of solar corona from really observed photospheric magnetogram. In this paper, a large-scale dataset of 3D solar magnetic fields of active regions is built by using the nonlinear force-free magnetic field (NLFFF) extrapolation from vector magnetograms of Helioseismic and Magnetic Imager (HMI) on Solar Dynamics Observatory (SDO). In this dataset, all space-weather HMI active region patches (SHARPs) with the corresponding serial numbers of national oceanic and atmospheric administration (NOAA) are included. They are downloaded from the SHARP 720 s series of JSOC every 96 minutes. In addition, each sample is labelled with a finer grained label for solar flare forecast. This paper is with the purpose of open availability of data resource and source code to the peers for refraining from repeated labor of data preparation. Meanwhile, with such a large-scale, high spatio-temporal resolution and high quality scientific data, we anticipate a wide attention and interest from artificial intelligence (AI) and computer vision communities, for exploring AI for astronomy over such a large-scale dataset.
Appears in Collections:信息工程学院

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