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Automated determination of electron density from electric field measurements on the Van Allen Probes spacecraft
Author(s) -
Zhelavskaya I. S.,
Spasojevic M.,
Shprits Y. Y.,
Kurth W. S.
Publication year - 2016
Publication title -
journal of geophysical research: space physics
Language(s) - English
Resource type - Journals
eISSN - 2169-9402
pISSN - 2169-9380
DOI - 10.1002/2015ja022132
Subject(s) - van allen probes , plasmasphere , electron density , spacecraft , artificial neural network , electric field , suite , computer science , number density , computational physics , physics , electron , algorithm , plasma , engineering , artificial intelligence , aerospace engineering , van allen radiation belt , nuclear physics , magnetosphere , astrophysics , quantum mechanics , archaeology , history
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.