Abstract
Machine learning techniques have been successfully introduced in the fields of Space Physics and Space Weather, yielding highly promising results in modeling and predicting many disparate aspects of the geospace environment. Magnetospheric ultra-low frequency (ULF) waves can have a strong impact on the dynamics of charged particles in the radiation belts, which can affect satellite operation. Here, we employ a method based on Fuzzy Artificial Neural Networks in order to detect ULF waves in the time series of the magnetic field measurements on board the low-Earth orbit CHAMP satellite. The outputs of the method are validated against a previously established, wavelet-based, spectral analysis tool, that was designed to perform the same task, and show encouragingly high scores in the detection and correct classification of these signals.
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