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太阳活动区磁图图像数据集,用于空间天气预报研究。

Solar active region magnetogram image dataset for studies of space weather.

机构信息

Klipsch School of Electrical and Computer Engineering, New Mexico State University, Las Cruces, NM, 88003, USA.

出版信息

Sci Data. 2023 Nov 24;10(1):825. doi: 10.1038/s41597-023-02628-8.

Abstract

In this dataset we provide a comprehensive collection of line-of-sight (LOS) solar photospheric magnetograms (images quantifying the strength of the photospheric magnetic field) from the National Aeronautics and Space Administration's (NASA's) Solar Dynamics Observatory (SDO). The dataset incorporates data from three sources and provides SDO Helioseismic and Magnetic Imager (HMI) magnetograms of solar active regions (regions of large magnetic flux, generally the source of eruptive events) as well as labels of corresponding flaring activity. This dataset will be useful for image analysis or solar physics research related to magnetic structure, its evolution over time, and its relation to solar flares. The dataset will be of interest to those researchers investigating automated solar flare prediction methods, including supervised and unsupervised machine learning (classical and deep), binary and multi-class classification, and regression. This dataset is a minimally processed, user configurable dataset of consistently sized images of solar active regions that can serve as a comprehensive image dataset of LOS photospheric magnetograms for solar flare prediction research.

摘要

在这个数据集中,我们提供了美国国家航空航天局(NASA)太阳动力学观测站(SDO)的全面的视线(LOS)太阳光球磁图(定量描述光球磁场强度的图像)集合。该数据集结合了来自三个来源的数据,并提供了 SDO 太阳震动力学和磁成像仪(HMI)太阳活动区(大磁通量区域,通常是爆发事件的来源)的磁图以及相应耀斑活动的标签。该数据集将有助于与磁结构、其随时间的演化以及与太阳耀斑的关系相关的图像分析或太阳物理研究。该数据集将引起研究人员的兴趣,他们研究自动太阳耀斑预测方法,包括监督和无监督机器学习(经典和深度学习)、二进制和多类分类以及回归。该数据集是一个经过最小处理、用户可配置的太阳活动区一致大小图像数据集,可以作为太阳耀斑预测研究的 LOS 光球磁图综合图像数据集。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5f01/10673907/375bd26b8f87/41597_2023_2628_Fig1_HTML.jpg

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