YNAO OpenIR  > 天文技术实验室
A deep learning method to estimate magnetic fields in solar active regions from photospheric continuum images
Bai, Xianyong1,2; Liu H(刘辉)3; Deng, Yuanyong1,2; Jiang, Jie4; Guo, Jingjing1,2; Bi Y(毕以)3; Feng, Tao5; Jin ZY(金振宇)3; Cao, Wenda6; Su, Jiangtao1,2; Ji KF(季凯帆)3
发表期刊ASTRONOMY & ASTROPHYSICS
2021-08-25
卷号652
DOI10.1051/0004-6361/202140374
产权排序第3完成单位
收录类别SCI ; EI
关键词Sun: magnetic fields Sun: photosphere methods: statistical
摘要

Context. The magnetic field is the underlying cause of solar activities. Spectropolarimetric Stokes inversions have been routinely used to extract the vector magnetic field from observations for about 40 years. In contrast, the photospheric continuum images have an observational history of more than 100 years. Aims. We suggest a new method to quickly estimate the unsigned radial component of the magnetic field, vertical bar B-r vertical bar, and the transverse field, B-t, just from photospheric continuum images (I) using deep convolutional neural networks (CNN). Methods. Two independent models, that is, I versus vertical bar B-r vertical bar and I versus B-t, are trained by the CNN with a residual architecture. A total of 7800 sets of data (I, B-r and B-t) covering 17 active region patches from 2011 to 2015 from the Helioseismic and Magnetic Imager are used to train and validate the models. Results. The CNN models can successfully estimate vertical bar B-r vertical bar as well as B-t maps in sunspot umbra, penumbra, pore, and strong network regions based on the evaluation of four active regions (test datasets). From a series of continuum images, we can also detect the emergence of a transverse magnetic field quantitatively with the trained CNN model. The three-day evolution of the averaged value of the estimated vertical bar B-r vertical bar and B-t from continuum images follows that from Stokes inversions well. Furthermore, our models can reproduce the nonlinear relationships between I and vertical bar B-r vertical bar as well as B-t, explaining why we can estimate these relationships just from continuum images. Conclusions. Our method provides an effective way to quickly estimate vertical bar B-r vertical bar and B-t maps from photospheric continuum images. The method can be applied to the reconstruction of the historical magnetic fields and to future observations for providing the quick look data of the magnetic fields.

资助项目US NSFNational Science Foundation (NSF)[AGS1821294] ; [12073077] ; [11873062] ; [11427901] ; [11873023] ; [11873027] ; [11729301] ; [11833010] ; [U2031140] ; [U1731241] ; [XDA15052200] ; [XDA15320302] ; [1916321TS00103201]
项目资助者US NSFNational Science Foundation (NSF)[AGS1821294] ; [12073077] ; [11873062] ; [11427901] ; [11873023] ; [11873027] ; [11729301] ; [11833010] ; [U2031140] ; [U1731241] ; [XDA15052200] ; [XDA15320302] ; [1916321TS00103201]
语种英语
学科领域天文学 ; 太阳与太阳系 ; 计算机科学技术 ; 人工智能
学科门类理学 ; 理学::天文学 ; 工学 ; 工学::计算机科学与技术(可授工学、理学学位)
文章类型Article
出版者EDP SCIENCES S A
出版地17, AVE DU HOGGAR, PA COURTABOEUF, BP 112, F-91944 LES ULIS CEDEX A, FRANCE
ISSN0004-6361
URL查看原文
WOS记录号WOS:000688233900007
EI入藏号20213510847350
EI主题词Deep learning
EI分类号512.1.2 Petroleum Deposits : Development Operations - 657.1 Solar Energy and Phenomena - 701.2 Magnetism: Basic Concepts and Phenomena
引用统计
被引频次:4[WOS]   [WOS记录]     [WOS相关记录]
文献类型期刊论文
版本出版稿
条目标识符http://ir.ynao.ac.cn/handle/114a53/24549
专题天文技术实验室
抚仙湖太阳观测和研究基地
通讯作者Ji KF(季凯帆)
作者单位1.Key Laboratory of Solar Activity, National Astronomical Observatories, Chinese Academy of Sciences, 20 Datun Road, Beijing 100101, PR China;
2.School of Astronomy and Space Science, University of Chinese Academy of Sciences, No. 19(A) Yuquan Road, Beijing 100049, PR China;
3.Yunnan Observatories, Chinese Academy of Sciences, Kunming, 650011 Yunnan, PR China;
4.School of Space and Environment, Beihang University, Beijing, PR China;
5.College of Computer Science, Sichuan University, Chengdu 610065, PR China;
6.Big Bear Solar Observatory, New Jersey Institute of Technology, Big Bear City, CA 92314-9672, USA
通讯作者单位中国科学院云南天文台
推荐引用方式
GB/T 7714
Bai, Xianyong,Liu H,Deng, Yuanyong,et al. A deep learning method to estimate magnetic fields in solar active regions from photospheric continuum images[J]. ASTRONOMY & ASTROPHYSICS,2021,652.
APA Bai, Xianyong.,Liu H.,Deng, Yuanyong.,Jiang, Jie.,Guo, Jingjing.,...&Ji KF.(2021).A deep learning method to estimate magnetic fields in solar active regions from photospheric continuum images.ASTRONOMY & ASTROPHYSICS,652.
MLA Bai, Xianyong,et al."A deep learning method to estimate magnetic fields in solar active regions from photospheric continuum images".ASTRONOMY & ASTROPHYSICS 652(2021).
条目包含的文件
文件名称/大小 文献类型 版本类型 开放类型 使用许可
A deep learning meth(4401KB)期刊论文出版稿开放获取CC BY-NC-SA浏览 请求全文
个性服务
推荐该条目
保存到收藏夹
查看访问统计
导出为Endnote文件
谷歌学术
谷歌学术中相似的文章
[Bai, Xianyong]的文章
[Liu H(刘辉)]的文章
[Deng, Yuanyong]的文章
百度学术
百度学术中相似的文章
[Bai, Xianyong]的文章
[Liu H(刘辉)]的文章
[Deng, Yuanyong]的文章
必应学术
必应学术中相似的文章
[Bai, Xianyong]的文章
[Liu H(刘辉)]的文章
[Deng, Yuanyong]的文章
相关权益政策
暂无数据
收藏/分享
文件名: A deep learning method to estimate magnetic fields in solar active regions from photospheric continuum images.pdf
格式: Adobe PDF
所有评论 (0)
暂无评论
 

除非特别说明,本系统中所有内容都受版权保护,并保留所有权利。