Radial diffusion coefficients database in the framework of the SafeSpace project: A Machine Learning model and the application to radiation belt simulations

Abstract

<p>Radial diffusion has been established as one of the most important mechanisms contributing to the acceleration and loss of relativistic electrons in the outer radiation belt. Over the past few years efforts have been devoted to identify empirical relationships of radial diffusion coefficients (D<sub>LL</sub>) for radiation belt simulations, yet several studies have suggested that the difference between the various models can be orders of magnitude different at high levels of geomagnetic activity, as the observed DLL have been shown to be highly event-specific. In the framework of the SafeSpace project we have used 12 years (2010 – 2020) of multi-point magnetic and electric field measurements from THEMIS A, D and E satellites to create a database of calculated DLL. In this work we present the statistics on the evolution of DLL during the solar cycle 24 with respect to the various solar wind parameters, geomagnetic indices and universal coupling functions. Furthermore, we show the importance of the use of event-specific DLL through simulations of seed and relativistic electrons with the Salammbo code during the intense storm of St. Patricks 2015 and the high-speed stream driven storm of Christmas 2013. Finally, we present a new approach for a Machine Learning model driven solely by Solar wind parameters.</p><p>This work has received funding from the European Union's Horizon 2020 research and innovation programme “SafeSpace” under grant agreement No 870437.</p>