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arXiv 2006.12835physics.space-phastro-ph.SR
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使用梯度推进回归改进环境太阳风模式预测

Using gradient boosting regression to improve ambient solar wind model predictions

R. L. Bailey, M. A. Reiss, C. N. Arge, C. Möstl, M. J. Owens, U. V. Amerstorfer, C. J. Henney, T. Amerstorfer, A. J. Weiss, J. Hinterreiter

摘要:

环境太阳风是一种由压力驱动的持续的太阳等离子体流,对它的研究是空间天气研究的一个重要组成部分。行星际空间中的太阳风流动决定了太阳风暴在到达地球之前如何通过日光层演变,特别是在太阳活动极小期,太阳风本身就是地球磁场活动的驱动力。因此,准确地预测环境太阳风流对空间天气意识至关重要。在这里,我们提出了一个机器学习的方法,其中解决方案从磁模型的日华被用来输出地球附近的太阳风的条件。在全面的验证分析中,将结果与观测结果和现有模型进行比较,新模型在几乎所有指标上都优于现有模型。此外,这种方法提供了一个新的视角来讨论不同的输入数据的环境太阳风建模的作用,这告诉我们什么样的基本物理过程。这里讨论的最后一个模型代表了一个非常快速,经过充分验证和开源的方法来预测地球上的环境太阳风。

英文摘要:

Studying the ambient solar wind, a continuous pressure-driven plasma flow emanating from our Sun, is an important component of space weather research. The ambient solar wind flows in interplanetary space determine how solar storms evolve through the heliosphere before reaching Earth, and especially during solar minimum are themselves a driver of activity in the Earth's magnetic field. Accurately forecasting the ambient solar wind flow is therefore imperative to space weather awareness. Here we present a machine learning approach in which solutions from magnetic models of the solar corona are used to output the solar wind conditions near the Earth. The results are compared to observations and existing models in a comprehensive validation analysis, and the new model outperforms existing models in almost all measures. In addition, this approach offers a new perspective to discuss the role of different input data to ambient solar wind modeling, and what this tells us about the underlying physical processes. The final model discussed here represents an extremely fast, well-validated and open-source approach to the forecasting of ambient solar wind at Earth.

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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