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基于非线性无力场方法外推得到的三维太阳磁场大样本数据集。

A Large-Scale Dataset of Three-Dimensional Solar Magnetic Fields Extrapolated by Nonlinear Force-Free Method.

机构信息

State Key Laboratory of Space Weather, National Space Science Center, Chinese Academy of Sciences, Beijing, 100190, China.

School of Astronomy and Space Science, University of Chinese Academy of Sciences, Beijing, 100049, China.

出版信息

Sci Data. 2023 Mar 30;10(1):178. doi: 10.1038/s41597-023-02091-5.

Abstract

It 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.

摘要

人们普遍认为,太阳磁场可以操纵所有太阳活动,尤其是太阳日冕中的剧烈太阳爆发。因此,从实际观测到的太阳色球矢量磁图中重建太阳日冕的三维(3D)磁场是非常重要的。在本文中,使用太阳动力学观测站(SDO)上的 Helioseismic 和磁成像仪(HMI)的矢量磁图对非线性无作用力磁场(NLFFF)进行外推,构建了一个包含大量活动区三维太阳磁场的数据集。在这个数据集中,所有空间天气 HMI 活动区补丁(SHARP)都包含了与美国国家海洋和大气管理局(NOAA)相对应的编号。它们是从每 96 分钟下载一次的 JSOC 的 SHARP 720s 系列中获取的。此外,每个样本都标记了一个更细粒度的标签,用于太阳耀斑预测。本文的目的是向同行开放数据资源和源代码,以避免重复的数据准备工作。同时,由于有如此大规模、高时空分辨率和高质量的科学数据,我们预计人工智能(AI)和计算机视觉社区会广泛关注和感兴趣,以探索在如此大规模数据集上的 AI 天文学。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/40c6/10063686/d264dba7570b/41597_2023_2091_Fig1_HTML.jpg

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