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首页> 外文期刊>Journal of Geophysical Research, A. Space Physics: JGR >Automatic Detection of the Thermal Electron Density From the WHISPER Experiment Onboard CLUSTER-II Mission With Neural Networks
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Automatic Detection of the Thermal Electron Density From the WHISPER Experiment Onboard CLUSTER-II Mission With Neural Networks

机译：电子自动检测的热密度的实验在低语CLUSTER-II使命与神经网络

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摘要

The Waves of HIgh frequency and Sounder for Probing Electron density by Relaxation (WHISPER) instrument has been monitoring the bulk properties of the plasma environment around Earth for more than 20 years. Onboard the 3-D Earth magnetospheric CLUSTER-II mission, this experiment delivers active and natural electric field spectra, in a frequency interval ranging respectively from 3.5 to 82 kHz, and from 2 to 80 kHz. The thermal electron density, a key parameter of scientific interest and major driver for the calibration of particles instrument, is derived from spectra. Until recently, the extraction of the thermal electron density required a manual intervention. To automate this process, self-learning algorithms based on Multilayer Neural Networks have been implemented. The evaluation of the thermal electron density from WHISPER spectra depends on the plasma region encountered by the spacecraft. First, a fully connected neural network has been implemented to predict the plasma region, using only the active spectra measured by the WHISPER instrument. Second, a specific neural network has been implemented to predict the thermal electron density for each plasma region. The model reaches up to 98% prediction accuracy for some plasma regimes. Two thermal electron density prediction models were trained, a first one to process data from the free solar wind and magnetosheath regions, and a second one for the plasmasphere region. The prediction accuracy can reach up to 95% in the free solar wind and magnetosheath regimes, and 75% in the plasmasphere.
机译：一波又一波的高频和测深仪探索电子密度的放松(耳语)仪器监测大部分地球周围等离子体环境的属性超过20年。磁性层的CLUSTER-II任务,实验提供了活跃和自然电场场光谱,频率间隔等分别从3.5到82千赫,从2到80人kHz。参数的科学兴趣和主要推动力粒子的校准仪器,来自光谱。提取热的电子密度需要手动干预。过程中,基于自学习算法多层神经网络实现。热的评价电子密度从光谱耳语取决于等离子体区域遇到的飞船。神经网络已实现连接预测等离子体区域,仅使用活跃光谱测量仪器欢悦地微语着。第二,一个特定的神经网络实现预测热电子每个等离子体密度区域。高达98%的预测精度对于一些等离子体政权。模型的训练,第一个来处理数据从免费的太阳风和磁鞘等离子体层的区域,而第二个地区。95%免费的太阳风和磁鞘政权,等离子体层的75%。

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