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

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,...

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Detalles Bibliográficos
Autores principales: Zhao, Zhongrui, Xu, Long, Zhu, Xiaoshuai, Zhang, Xinze, Liu, Sixuan, Huang, Xin, Ren, Zhixiang, Tian, Yonghong
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10063686/
https://www.ncbi.nlm.nih.gov/pubmed/36997553
http://dx.doi.org/10.1038/s41597-023-02091-5
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author Zhao, Zhongrui
Xu, Long
Zhu, Xiaoshuai
Zhang, Xinze
Liu, Sixuan
Huang, Xin
Ren, Zhixiang
Tian, Yonghong
author_facet Zhao, Zhongrui
Xu, Long
Zhu, Xiaoshuai
Zhang, Xinze
Liu, Sixuan
Huang, Xin
Ren, Zhixiang
Tian, Yonghong
author_sort Zhao, Zhongrui
collection PubMed
description 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.
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spelling pubmed-100636862023-04-01 A Large-Scale Dataset of Three-Dimensional Solar Magnetic Fields Extrapolated by Nonlinear Force-Free Method Zhao, Zhongrui Xu, Long Zhu, Xiaoshuai Zhang, Xinze Liu, Sixuan Huang, Xin Ren, Zhixiang Tian, Yonghong Sci Data Data Descriptor 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. Nature Publishing Group UK 2023-03-30 /pmc/articles/PMC10063686/ /pubmed/36997553 http://dx.doi.org/10.1038/s41597-023-02091-5 Text en © The Author(s) 2023 https://creativecommons.org/licenses/by/4.0/Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) .
spellingShingle Data Descriptor
Zhao, Zhongrui
Xu, Long
Zhu, Xiaoshuai
Zhang, Xinze
Liu, Sixuan
Huang, Xin
Ren, Zhixiang
Tian, Yonghong
A Large-Scale Dataset of Three-Dimensional Solar Magnetic Fields Extrapolated by Nonlinear Force-Free Method
title A Large-Scale Dataset of Three-Dimensional Solar Magnetic Fields Extrapolated by Nonlinear Force-Free Method
title_full A Large-Scale Dataset of Three-Dimensional Solar Magnetic Fields Extrapolated by Nonlinear Force-Free Method
title_fullStr A Large-Scale Dataset of Three-Dimensional Solar Magnetic Fields Extrapolated by Nonlinear Force-Free Method
title_full_unstemmed A Large-Scale Dataset of Three-Dimensional Solar Magnetic Fields Extrapolated by Nonlinear Force-Free Method
title_short A Large-Scale Dataset of Three-Dimensional Solar Magnetic Fields Extrapolated by Nonlinear Force-Free Method
title_sort large-scale dataset of three-dimensional solar magnetic fields extrapolated by nonlinear force-free method
topic Data Descriptor
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10063686/
https://www.ncbi.nlm.nih.gov/pubmed/36997553
http://dx.doi.org/10.1038/s41597-023-02091-5
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