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\begin{document}

\title{Radiation belt model including semi-annual variation and Solar driving
(Sentinel)}



\author[1]{Christos Katsavrias}%
\author[2]{Sigiava Aminalragia-Giamini}%
\author[3]{Constantinos Papadimitriou}%
\author[4]{Ioannis A. Daglis}%
\author[2]{Ingmar Sandberg}%
\author[5]{Piers Jiggens}%
\affil[1]{University of Athens}%
\affil[2]{Space Applications and Research Consultancy}%
\affil[3]{National Observatory of Athens}%
\affil[4]{National and Kapodistrian University of Athens}%
\affil[5]{European Space Agency}%


\vspace{-1em}



  
  \date{June 13, 2024}


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\begin{abstract}
The Earth's outer radiation belt response to geospace disturbances is
extremely variable spanning from a few hours to several months. In
addition, the numerous physical mechanisms, which control this response,
depend on the electron energy, the time-scale and the various types of
geospace disturbances. As a consequence, the various models that
currently exist are either specialized, orbit-specific data-driven
models, or sophisticated physics-based ones. In this paper we present a
new approach for radiation belt modelling using Machine Learning methods
driven solely by solar wind speed and pressure, Solar flux at 10.7 cm
and the \$\textbackslash{}theta\$ angle controlling the
Russell-McPherron effect. We show that the model can successfully
reproduce and predict the electron fluxes of the outer radiation belt in
a broad energy (0.033--4.062 MeV) and L-shell (2.5--5.9) range and,
moreover, it can capture the long-term modulation of the semi-annual
variation. We also show that the model can generalize well and provide
successful predictions, even outside of the spatio-temporal range it has
been trained with, using \textgreater{}0.8 MeV electron flux
measurements from GOES-15/EPEAD at geostationary orbit.%
\end{abstract}%



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