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\begin{document}

\title{Prediction of Plasma Pressure in the Outer Part of the Inner
Magnetosphere using Machine Learning}



\author[1]{Songyan Li}%
\author[2]{Elena A. Kronberg}%
\author[3]{Christopher G. Mouikis}%
\author[1]{Hao Luo}%
\author[4]{Yasong S Ge}%
\author[5]{Aimin Du}%
\affil[1]{Institute of Geology and Geophysics, Chinese Academy of Sciences}%
\affil[2]{Ludwig Maximilian University of Munich}%
\affil[3]{University of New Hampshire}%
\affil[4]{Institute of Geology and Geophysics}%
\affil[5]{Institue of Geology and Geophysics, Chinese Academy of Sciences}%


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  \date{May 25, 2024}


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\begin{abstract}
The information on plasma pressures in the outer part of the inner
magnetosphere is important for\\
simulations of the inner magnetosphere and the better understanding of
its dynamics. Based on 17-year\\
observations from both CIS and RAPID instruments onboard the Cluster
mission, we used machine-\\
learning-based models to predict proton plasma pressures at energies
from \textasciitilde{}40eV to 4MeV in the outer\\
part of the inner magnetosphere (L*=5-9). The location in the
magnetosphere, and parameters of solar,\\
solar wind, and geomagnetic activity from the OMNI database are used as
predictors. We trained several\\
different machine-learning-based models and compared their performances
with observations. The\\
results demonstrate that the Extra-Trees Regressor has the best
predicting performance. The Spearman\\
correlation between the observations and predictions by the model data
is about 68\%. The most\\
important parameter for predicting proton pressures in our model is the
L* value, which is related to the\\
location. The most important predictor of solar and geomagnetic activity
is the solar wind dynamic pressure. Based on the observations and
predictions by our model, we find that no matter under quiet or
disturbed geomagnetic conditions, both the dusk-dawn asymmetry at the
dayside with higher pressures at the duskside and the day-night
asymmetry with higher pressures at the nightside occur. Our results have
direct practical applications, for instance, inputs for simulations of
the inner magnetosphere or the reconstruction of the 3-D magnetospheric
electric current system based on the magnetostatic equilibrium, and can
also provide valuable guidance to the space weather forecast.%
\end{abstract}%



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