Solar Wind Control of Radiation Belt Electron Flux

太阳风对辐射带电子通量的控制

• 批准号: 1938087
• 金额: --
• 依托单位: University of Reading
• 依托单位国家: 英国
• 项目类别: Studentship
• 财政年份: 2017
• 资助国家: 英国
• 起止时间: 2017 至 无数据
• 项目状态: 已结题
• 来源: https://gtr.ukri.org/projects?ref=studentship-1938087
• 关键词: Solar; Wind; Control; Radiation; Belt

Our increasing reliance upon space-based technologies (communications, location-finding, etc) means that predicting the "Space Weather" of Earth's Radiation Belts is very desirable. The high-energy electron flux within Earth's Outer Radiation Belt is highly variable on timescales of hours to days, but we do not yet know the major factors that control this variability. It is clear that important solar wind transient features, such as coronal mass ejections and stream interaction regions can alter the size and strength of the Outer Radiation Belt, but the changes are difficult to predict. Spacecraft anomalies including electrostatic discharges and single-event upsets are related to increases in the density of high-energy electrons in the spacecraft environment. The Outer Radiation Belt spans the distance from around 2.5 Earth radii from the centre of the Earth to beyond geosynchronous orbit, at 6.6 Earth radii, but until recently there have been few opportunities to scientifically sample the high-energy electron environment, principally because it is so hazardous to spacecraft and electronic instruments. Physics-based numerical models of the Outer Radiation Belt are in their infancy. It is a significant challenge to reduce the complex plasma physics of collisionless, relativistic electron dynamics to a numerical system that can be solved on the necessary timescales. This project will provide valuable insight into the most important physical processes linking the solar wind the Outer Radiation Belt that can be used to build such a physics-based model in the future.There are two key novelties in the planned project. First, we will use data from the NASA Van Allen Probes mission to provide information on the variability of electron flux across a range of different distances from the Earth. This mission (launched in 2013) will provide the project with at least four years of in-situ space plasma data in unprecedented detail and in multiple locations simultaneously throughout the Outer Radiation Belt. Previous attempts to investigate the relationship between solar wind variability and Radiation Belt fluxes have been restricted to locations near geosynchronous orbit due to a lack of in situ data from other locations. Spacecraft data from the solar wind and from the Van Allen Probes will be combined to investigate how variability in the solar wind relates to variability in the Earth's Radiation Belts and whether there are repeatable patterns in both that may be predicted successfully.Second, cutting-edge machine learning techniques will be used to investigate the relationship between solar wind variability and the electron flux and which sets of parameters best predict future radiation belt conditions. Machine-learning techniques can be used to find repeatable patterns in empirical data and then build them into predictive models. Solar wind parameters such as speed, number density and magnetic field orientation all contribute to changes in the Earth's magnetosphere, and especially in the Outer Radiation Belts, but they also exhibit inter-dependencies due to the physics of the solar wind. We will begin by studying patterns in the solar wind variability, where techniques will be developed for time series at a single point in space. Later, these techniques will be employed to build models of the variability of electron flux over a range of distances from the Earth, based upon inputs from the solar wind data. Carefully interpreted, machine-learning techniques allow us to determine those parameters that most influence the changing electron flux and provide indispensable clues for the physical mechanisms by which they exert that influence. By judiciously interpreting the results from machine-learning algorithms in the framework of space plasma physics, it is hoped to gain new insight into how the solar wind controls the variability of the whole Outer Radiation Belt.

我们对天基技术（通信、定位等）的日益依赖意味着预测地球辐射带的“太空天气”是非常有必要的。地球外辐射带内的高能电子通量在几小时到几天的时间尺度上变化很大，但我们还不知道控制这种变化的主要因素。很明显，重要的太阳风瞬态特征，例如日冕物质抛射和流相互作用区域，可以改变外辐射带的大小和强度，但这些变化很难预测。包括静电放电和单粒子扰乱在内的航天器异常现象与航天器环境中高能电子密度的增加有关。外辐射带横跨从地球中心约 2.5 个地球半径到地球同步轨道外 6.6 个地球半径的距离，但直到最近，对高能电子环境进行科学采样的机会还很少，主要是因为它是对航天器和电子仪器非常危险。基于物理的外辐射带数值模型尚处于起步阶段。将无碰撞、相对论电子动力学的复杂等离子体物理简化为可以在必要的时间尺度上求解的数值系统是一项重大挑战。该项目将为连接太阳风和外辐射带的最重要的物理过程提供有价值的见解，可用于在未来构建这样一个基于物理的模型。计划中的项目有两个关键的新颖之处。首先，我们将使用来自 NASA 范艾伦探测器任务的数据来提供有关距地球不同距离范围内电子通量变化的信息。该任务（2013 年启动）将为该项目提供至少四年的现场空间等离子体数据，其详细程度前所未有，并且在整个外辐射带的多个地点同时进行。由于缺乏其他位置的现场数据，之前研究太阳风变化与辐射带通量之间关系的尝试仅限于地球同步轨道附近的位置。来自太阳风和范艾伦探测器的航天器数据将被结合起来，以研究太阳风的变化与地球辐射带的变化之间的关系，以及两者是否存在可成功预测的可重复模式。机器学习技术将用于研究太阳风变化与电子通量之间的关系，以及哪些参数组最能预测未来的辐射带状况。机器学习技术可用于查找经验数据中的可重复模式，然后将其构建为预测模型。速度、数量密度和磁场方向等太阳风参数都会导致地球磁层的变化，尤其是外辐射带的变化，但由于太阳风的物理特性，它们也表现出相互依赖性。我们将从研究太阳风变化的模式开始，其中将开发空间中单个点的时间序列技术。随后，这些技术将用于根据太阳风数据的输入，建立距地球一定距离范围内电子通量变化的模型。经过仔细解释，机器学习技术使我们能够确定那些对电子通量变化影响最大的参数，并为它们发挥这种影响的物理机制提供不可或缺的线索。通过在空间等离子体物理框架下明智地解释机器学习算法的结果，希望能够对太阳风如何控制整个外辐射带的变化获得新的见解。