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

Zhelavskaya I. S., M. Spasojevic, Y. Y. Shprits, W. S. Kurth, (2016), Automated determination of electron density from electric field measurements on the Van Allen Probes spacecraft, J. of Geophys. Res. [Space Physics], 121, 4611-4625, doi:10.1002/2015JA022132

Abstract

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 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.

Authors (sorted by name)

Shprits Spasojevic Zhelavskaya

Journal / Conference

Journal Of Geophysical Research (Space Physics)

Acknowledgments

The work at UCLA is supported by NASA awards NNX10AK99G and NNX13AE34G, NSF award AGS‐1243183, UC Lab Fee award 116720, and Horizon 2020 award 637302, the International Space Science Institute (ISSI). The work at Stanford University was supported under NASA awards NNX14AC04G and NNX15AI94G. The research at University of Iowa was supported by JHU/APL contract 921647 under NASA prime contract NAS5‐01072. All Van Allen Probes data were accessed through the EMFISIS website hosted at the University of Iowa, and we graciously thank the EMFISIS Principal Investigator, Craig Kletzing, and the EMFISIS team. The Kp index was provided by the World Data Center. The electron density data set obtained in this paper is available from ftp://rbm.epss.ucla.edu/ftpdisk1/NURD.

Grants

116720 637302 921647 AGS‐1243183 NAS5‐01072 NNX10AK99G NNX13AE34G NNX14AC04G NNX15AI94G

Bibtex

@article{doi:10.1002/2015JA022132,
author = {Zhelavskaya, I. S. and Spasojevic, M. and Shprits, Y. Y. and Kurth, W. S.},
title = {Automated determination of electron density from electric field measurements on the Van Allen Probes spacecraft},
year={2016},
journal = {Journal of Geophysical Research: Space Physics},
volume = {121},
number = {5},
pages = {4611-4625},
keywords = {Van Allen Probes, electron number density, neural networks},
doi = {10.1002/2015JA022132},
url = {https://agupubs.onlinelibrary.wiley.com/doi/abs/10.1002/2015JA022132},
eprint = {https://agupubs.onlinelibrary.wiley.com/doi/pdf/10.1002/2015JA022132},
abstract = {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 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.}
}