Global distribution and evolution of whistler mode chorus and hiss waves studied by a machine learning based model

Abstract:

The Earth’s inner magnetosphere is a complex, non-linear, and interconnect system that is driven by external solar wind driving and internal processes. A neural network approach has been proposed to reconstruct and predict the complexity of the inner magnetospheric environment [Bortnik 2016; 2018], including the cold plasma density [Chu et al., 2017a; b], the plasma waves such as the whistler chorus and hiss waves, electromagnetic ion cyclotron (EMIC) and ultra-low frequency (ULF) waves. We present the machine learning (ML) based empirical models of the wave environment in the inner magnetosphere. The ML-based wave model used a neural network approach, which takes the solar wind parameters and geomagnetic indices as input parameters and is able to provide a global reconstruction and prediction of the wave environment in the inner magnetosphere (L<7). The model performance has been validated and tested on out-of-sample data sets which have never been ‘seen’ by the model, thereby demonstrating the model provides reliable and stable predictions. We show that the reconstructed wave environment appears qualitatively realistic for a range of geomagnetic activities, including geomagnetic storms and substorms. The characteristics of the temporal and spatial evolution of the wave environment are investigated using the ML-based reconstruction of the wave environment, which show interesting results. The results show how machine learning technique might be used to help achieve new discoveries in magnetospheric physics, as well as advance state-of-the-art space weather prediction.