Can XMM-Newton Be Used to Track Compositional Changes in the Solar Wind?

IF 2.6 2区 地球科学 Q2 ASTRONOMY & ASTROPHYSICS Journal of Geophysical Research: Space Physics Pub Date : 2024-12-11 DOI:10.1029/2024JA033323
S. Nitti, J. A. Carter, S. F. Sembay, S. E. Milan, L. Zhao, S. T. Lepri, K. D. Kuntz
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Abstract

Geocoronal Solar Wind Charge Exchange (SWCX) is the process by which heavy ions from the solar wind undergo charge exchange with neutral hydrogen atoms from the Earth's exosphere, releasing photons at discrete energies characteristic of the solar wind ions. This paper investigates the solar wind types driving geocoronal SWCX. We find that during periods of time-variable SWCX, higher fractions of every ion species are recorded by ACE compared to the averages. Notably, a subset of the slow solar wind characterized by a systematic lower temperature and higher proton flux is surprisingly effective for producing SWCX. Given the degradation of the solar wind composition spectrometer on ACE in 2011, we explore the capabilities of XMM-Newton as an alternative sensor to monitor heavy ion composition in the solar wind. Unlike the distributions of other ion line fluxes analyzed, only OVIII, extracted via spectral analysis of XMM-Newton observations, display patterns similar to the corresponding parent ion abundances from ACE O 8 + / p $\left({\mathrm{O}}^{\mathrm{8}+}/\mathrm{p}\right)$ . Finally, we employ a Random Forest Classifier model to predict solar wind types based on literature results. When combining proton data with XMM-Newton features, the model performance improves significantly, achieving a macro-averaged F1 score of 0.80 (with a standard deviation of 0.06).

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xmm -牛顿能否用于追踪太阳风成分的变化?
日冕太阳风电荷交换(SWCX)是太阳风中的重离子与地球外逸层中的中性氢原子进行电荷交换,释放出具有太阳风离子特征的离散能量光子的过程。本文研究了驱动日冕SWCX的太阳风类型。我们发现,在随时间变化的SWCX期间,ACE记录的每种离子种类的分数都高于平均值。值得注意的是,慢太阳风的一个子集以系统的较低温度和较高的质子通量为特征,对产生SWCX非常有效。鉴于2011年ACE上太阳风成分光谱仪的退化,我们探索了xmm -牛顿作为监测太阳风重离子成分的替代传感器的能力。与分析的其他离子线通量分布不同,只有通过XMM-Newton观测的光谱分析提取的OVIII,显示出与ACE o8 + / p对应的母体离子丰度相似的模式美元\离开({\ mathrm {O}} ^ {\ mathrm {8} +} / \ mathrm p {} \ )$ .最后,基于文献结果,采用随机森林分类器模型对太阳风类型进行预测。当质子数据与XMM-Newton特征相结合时,模型性能得到显著提高,宏观平均F1得分为0.80(标准差为0.06)。
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来源期刊
Journal of Geophysical Research: Space Physics
Journal of Geophysical Research: Space Physics Earth and Planetary Sciences-Geophysics
CiteScore
5.30
自引率
35.70%
发文量
570
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