Constraining the Location of the Outer Boundary of Earth's Outer Radiation Belt

Constraining the Location of the Outer Boundary of Earth's Outer Radiation Belt
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约束地球外辐射带外边界的位置

DOI:
10.1002/essoar.10505703.1
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发表时间:
2021
期刊:
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通讯作者:
Bloch T
Bloch T
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作者:
Bloch T

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确定外辐射带外边界的位置是改进辐射带模型的一个关键方面,有助于限制我们对源电子和种子电子居群进入辐射带的机制的理解。在本文中,我们假设辐射带外边界的电子分布函数存在统计差异,从而分析来自THEMIS(事件时间历史和亚风暴期间宏观尺度相互作用)卫星的电子通量数据来确定这一位置。我们通过使用模拟电子L*值来近似表征辐射带内外电子分布函数之间的差异,从而验证了我们的假设。首先,我们通过研究电子分布函数的径向演化进行了简单的统计分析。这种方法不会产生明显的不连续性,因此强调需要对数据进行更复杂的统计处理。随后,我们使用机器学习(不依赖于径向位置或L*)来测试一系列候选外边界位置。通过分析模型在每个候选位置的性能,我们确定了一个≈8RE的统计边界,结果表明存在一定的可变性。这一统计边界通常比当前辐射带模型中使用的统计边界更远。
Characterizing the location of the outer boundary of the outer radiation belt is a key aspect of improving radiation belt models and helps to constrain our understanding of the mechanisms by which the source and seed electron populations are transported into the radiation belts. In this paper, we hypothesize that there are statistical differences in the electron distribution function across the radiation belt outer boundary, and thus analyze electron flux data from the THEMIS (Time History of Events and Macroscale Interactions during Substorms) satellites to identify this location. We validate our hypothesis by using modeled electron L* values to approximately characterize the differences between electron distribution functions inside and outside of the radiation belts. Initially, we perform a simple statistical analysis by studying the radial evolution of the electron distribution functions. This approach does not yield a clear discontinuity, thus highlighting the need for more complex statistical treatment of the data. Subsequently, we employ machine learning (with no dependence on radial position or L*) to test a range of candidate outer boundary locations. By analyzing the performance of the models at each candidate location, we identify a statistical boundary at ≈8RE, with results suggesting some variability. This statistical boundary is typically further out than those used in current radiation belt models.