arXivDaily arXiv每日学术速递 周一至周五更新
arXiv 2212.01730astro-ph.SRstat.ML
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利用极场资料改进太阳耀斑预报

Incorporating Polar Field Data for Improved Solar Flare Prediction

Mehmet Aktukmak, Zeyu Sun, Monica Bobra, Tamas Gombosi, Ward B. Manchester, Yang Chen, Alfred Hero

摘要:

在本文中,我们考虑将数据与太阳的北极和南极磁场强度,以提高太阳耀斑预测性能,使用机器学习模型。当用于补充来自太阳光球磁场上的活动区域的局部数据时,极场数据向预测器提供全局信息。虽然这种全球性的功能已经提出了预测下一个太阳活动周期的强度,在本文中,我们建议使用它们来帮助分类个人太阳耀斑。我们使用HMI数据进行实验,采用四种不同的机器学习算法,可以利用极场信息。此外,我们提出了一种新的概率混合专家模型,可以简单有效地将极场数据,并提供与最先进的太阳耀斑预测算法,如递归神经网络(RNN)的预测性能。我们的实验结果表明,极场数据的太阳耀斑预测的有用性,它可以提高高达10.1%的Heidke技能评分(HSS 2)。

英文摘要:

In this paper, we consider incorporating data associated with the sun's north and south polar field strengths to improve solar flare prediction performance using machine learning models. When used to supplement local data from active regions on the photospheric magnetic field of the sun, the polar field data provides global information to the predictor. While such global features have been previously proposed for predicting the next solar cycle's intensity, in this paper we propose using them to help classify individual solar flares. We conduct experiments using HMI data employing four different machine learning algorithms that can exploit polar field information. Additionally, we propose a novel probabilistic mixture of experts model that can simply and effectively incorporate polar field data and provide on-par prediction performance with state-of-the-art solar flare prediction algorithms such as the Recurrent Neural Network (RNN). Our experimental results indicate the usefulness of the polar field data for solar flare prediction, which can improve Heidke Skill Score (HSS2) by as much as 10.1%.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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