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\begin{document}

\title{Classifying Interplanetary Discontinuities Using Supervised Machine
Learning}



\author[1]{Daniel Dumitru}%
\author[2]{Costel Munteanu}%
\affil[1]{National Institute for Laser, Plasma and Radiation Physics}%
\affil[2]{Institute of Space Science}%


\vspace{-1em}



  
  \date{May 25, 2024}


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\begin{abstract}
Directional discontinuities (DDs) are defined as abrupt changes of the
magnetic field orientation. We use observations from ESA's Cluster
mission to compile a database of events: 4216 events are identified in
January-April 2007, and 5194 in January-April 2008. Localized time-scale
images depicting angular changes are created for each event, and a
preliminary classification algorithm is designed to distinguish between:
simple - isolated events, and complex - multiple overlapping events. In
2007, 1806 events are pre-classified as simple, and 2410 as complex; in
2008, 1997 events are simple, and 3197 are complex. A supervised machine
learning approach is used to recognize and predict these events. Two
models are trained: one for 2007, which is used to predict the results
in 2008, and vice-versa for 2008. To validate our results, we
investigate the discontinuity occurrence rate as a function of
spacecraft location. When the spacecraft is in the solar wind, we find
an occurrence rate of \textasciitilde{}2 DDs per hour and a 50/50 \%
ratio of simple/complex events. When the spacecraft is in the Earth's
magnetosheath, we find that the total occurrence rate remains around 2
DDs/h, but the ratio of simple/complex events changes to
\textasciitilde{}25/75 \%. This implies that about half of the simple
events observed in the solar wind are classified as complex when
observed in the magnetosheath. This demonstrates that our classification
scheme can provide meaningful insights, and thus be relevant for future
studies on interplanetary discontinuities.%
\end{abstract}%



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