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Automated determination of electron density fromelectric field measurements on the Van Allen Probes spacecraft

机译:通过Van Allen Probes航天器上的电场测量自动确定电子密度

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摘要

We present the Neural-network-based Upper hybrid Resonance Determination (NURD) algorithm for automatic inference of the electron number density from plasma wave measurements made on board NASA's Van Allen Probes mission. A feedforward neural network is developed to determine the upper hybrid resonance frequency, f_(uhr), from electric field measurements, which is then used to calculate the electron number density. In previous missions, the plasma resonance bands were manually identified, and there have been few attempts to do robust, routine automated detections. We describe the design and implementation of the algorithm and perform an initial analysis of the resulting electron number density distribution obtained by applying NURD to 2.5 years of data collected with the Electric and Magnetic Field Instrument Suite and Integrated Science (EMFISIS) instrumentation suite of the Van Allen Probes mission. Densities obtained by NURD are compared to those obtained by another recently developed automated technique and also to an existing empirical plasmasphere and trough density model.
机译:我们介绍了基于神经网络的上层混合共振确定(NURD)算法,用于根据NASA的Van Allen Probes任务进行的等离子波测量自动推断电子数密度。建立了前馈神经网络,从电场测量结果确定上混合共振频率f_(uhr),然后将其用于计算电子数密度。在以前的任务中,手动识别了等离子体共振带,几乎没有尝试进行可靠的常规自动检测。我们描述了算法的设计和实现,并对通过将NURD应用到Van的电场和磁场仪器套件和集成科学(EMFISIS)仪器套件收集的2.5年数据中获得的电子数密度分布进行了初步分析。 Allen Probes任务。将通过NURD获得的密度与通过另一项最新开发的自动化技术获得的密度进行比较,并与现有的经验等离子层和谷密度模型进行比较。

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