来自H-alpha观测的手动注释的纤维的数据集
Azim Ahmadzadeh1, Rohan Adhyapak2, Kartik Chaurasiya2
1University of Missouri-St. Louis, Department of Computer Science, St. Louis, MO, 63121, USA. ahmadzadeh@umsl.edu.
Scientific data
|September 27, 2024
概括
MAGFiLO数据集提供了10,244个注释的太阳光纤,使先进的深度学习能够精确识别特征和大规模太阳物理分析.
科学领域:
- 太阳物理 太阳物理
- 天文学 天文学
- 机器学习 机器学习
背景情况:
- 太阳线丝是太阳大气中的关键结构,对于理解磁活动至关重要.
- 以前的数据集缺乏全面分析和先进建模所需的规模.
- 精确注释导线属性,包括磁场性,是必不可少的.
研究的目的:
- 在H-alpha观测 (MAGFiLO v1.0) 数据集中引入手动注释的GONG线程.
- 为太阳能导线研究提供大规模,高质量的数据集.
- 为了实现太阳物理深度学习模型的进步.
主要方法:
- 从1593个全球振荡网络集团 (GONG) 观测 (2011-2022) 中收集了10244个丝状注释.
- 详细的注释包括细分,边界框,脊柱和磁场性.
- 通过超过1000人小时的注释和双盲审查过程来确保数据质量,达到0.66.6的卡帕得分.
主要成果:
- MAGFiLO v1.0 是同类数据中最大的数据集,在导线特征识别中提供了前所未有的精度.
- 经过验证的半球光纤偏好与之前的小规模研究的一致性.
- 证明了数据集的实用性,作为大规模太阳能丝分析的测试台.
结论:
- 马格菲洛数据集显著提升了太阳物理领域深度学习应用的潜力.
- 为太阳能物理学家研究大规模光纤行为和磁场特性提供了强大的资源.
- 建立了对注释式太阳能灯丝数据的规模和质量的新基准.
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