Automatic Detection of the Thermal Electron Density From the WHISPER Experiment Onboard CLUSTER‐II Mission With Neural Networks

• Published in: Journal of geophysical research. Space physics 2021-03, Vol.126 (3), p.n/a
• Main Authors: Gilet, N., De Leon, E., Gallé, R., Vallières, X., Rauch, J.‐L., Jegou, K., Bucciantini, L., Savreux, V., Décréau, P., Henri, P.
• Format: Article
• Language: English
• Subjects: Algorithms; Bulk density; Clusters; Earth magnetosphere; Electric fields; Electron density; Electrons; extraction of the thermal electron density; Machine learning; Magnetosheath; magnetospheric plasma; Model accuracy; Multilayers; Neural networks; Plasma; Plasmasphere; Prediction models; prediction of plasma regions; Sciences of the Universe; Solar wind; Spacecraft; Spectra
• ISSN: 2169-9380; 2169-9402
• DOI: 10.1029/2020JA028901

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. Key Points We have applied self‐learning methods to predict the key plasma regions crossed by the CLUSTER‐II spacecraft using the Waves of HIgh frequency and Sounder for Probing Electron density by Relaxation (WHISPER) instrument The extraction of the thermal electron density from WHISPER active (sounding mode) and natural (passive mode) electric field spectra is automatically done in the free solar wind, in the magnetosheath region and in the plasmasphere Such automatic procedure could be used for future data processing of electric field experiments onboard space missions (for instance AM2P onboard BepiColombo or MIME onboard JUICE)