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Authors: Zhelavskaya Irina S., Shprits Yuri Y, and ć Maria
Title: Empirical modeling of the plasmasphere dynamics using neural networks
Abstract: We propose a new empirical model for reconstructing the global dynamics of the cold plasma density distribution based only on solar wind data and geomagnetic indices. Utilizing the density database obtained using the NURD (Neural-network-based Upper hybrid Resonance Determination) algorithm for the period of October 1, 2012 - July 1, 2016, in conjunction with solar wind data and geomagnetic indices, we develop a neural network model that is capable of globally reconstructing the dynamics of the cold plasma density distribution for 2≤L≤6 and all local times. We validate and test the model by measuring its performance on independent datasets withheld from the training set and by comparing the model predicted global evolution with global images of He+ distribution in the Earth's plasmasph. . .
Date: 10/2017 Publisher: Journal of Geophysical Research: Space Physics DOI: 10.1002/2017JA024406 Available at:
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Authors: Zhelavskaya I. S., Spasojevic M., Shprits Y Y, and Kurth W S
Title: Automated determination of electron density from electric field measurements on the Van Allen Probes spacecraft
Abstract: 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, fuhr, 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. . .
Date: 05/2016 Publisher: Journal of Geophysical Research: Space Physics DOI: 10.1002/2015JA022132 Available at:
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