使用基于变压器的框架进行太阳耀斑的操作预测
Yasser Abduallah1,2, Jason T L Wang3,4, Haimin Wang1,5,6
1Institute for Space Weather Sciences, New Jersey Institute of Technology, University Heights, Newark, NJ, 07102-1982, USA.
Scientific reports
|August 22, 2023
概括
太阳耀斑是影响技术的太阳爆炸. 一个新的基于变压器的AI,SolarFlareNet,准确地预测太阳耀斑 (M5.0,M,C类) 提前24-72小时,帮助太空天气准备.
科学领域:
- 太阳物理 太阳物理
- 太空天气 太空天气
- 机器学习 机器学习
背景情况:
- 太阳耀斑是来自太阳活跃区域 (ARs) 的突然能量释放.
- 这些事件,以及冠状质量喷射,导致太空天气,破坏无线电通信和电网等技术.
- 准确的太阳耀斑预测对于灾害风险管理和技术准备至关重要.
研究的目的:
- 开发和介绍SolarFlareNet,这是一个基于变压器的框架,用于预测太阳耀斑.
- 预测活动区域在24至72小时内产生M5.0,M或C级火焰的可能性.
- 通过先进的机器学习来增强太空天气预报能力.
主要方法:
- 利用基于变压器的深度学习架构 (SolarFlareNet) 以时间序列来建模太阳能活跃区域数据.
- 训练了三个单独的变压器,每一个都专门用于预测特定的太阳耀斑类别 (M5.0,M,C).
- 包含来自空间天气HMI主动区域补丁 (SHARP) 的磁性参数和来自NCEI火焰目录的火焰数据 (2010年5月 - 2022年12月).
主要成果:
- 开发了一个完全可操作的SolarFlareNet系统,能够近乎实时进行太阳耀斑预测.
- 成功建模了太阳能活跃区域数据中的时间动态,使用变压器提高了预测准确度.
- 将预测模型扩展到基于校准的概率预测方法.
结论:
- 太阳耀斑网 (SolarFlareNet) 提供了重要的太阳耀斑的准确,早期预测,对于减轻太空天气影响至关重要.
- 基于变压器的方法有效地捕捉了太阳活动数据中复杂的时间模式.
- 该系统的运行状态和网络可访问性使其能够实现近乎实时的太空天气监测和预报.
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