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A Data-Driven Understanding of Plasma Transport in Saturn's Magnetic Environment
Azari, Abigail
Azari, Abigail
2020
Abstract: In 2004 the Cassini-Huygens mission arrived at Saturn. As the first ever Saturn
orbiter, Cassini collected data reaching from the largest moon, Titan, at 20 Saturn
radii (Rs), to the atmosphere during its death plunge in 2017. This mission drastically
shifted our understanding of the Saturn system by providing insights of complex
dynamics for over a decade. One of the major findings was of cryo-volcanic geysers
on Enceladus at 4 Rs, deep in the region dominated by Saturn’s magnetic field, or
magnetosphere. The water from Enceladus is one of the major factors leading to an
instability of charged particles, or plasma, called interchange. Interchange is most
similar to a Rayleigh-Taylor instability, in which the rapid rotation of Saturn drives
dense plasma into less dense H+, resulting in inward moving high-energy plasma, and
outward moving dense plasma. Interchange has long been expected as a process of
plasma transport throughout planetary magnetospheres and due to Cassini, statistical
studies are now able to answer in new detail questions about interchange’s role in
magnetospheric dynamics including plasma transport, energization, and loss.
In this thesis I present a supervised physics-based classification of interchange from
high-energy (3-220 keV) ions using methods commonly employed in machine learning
merged with physical knowledge of Saturn’s environment. With this standardized
list, subsequent work can be broken into four advancements toward understanding
Saturn’s plasma dynamics. First, this thesis developed estimations of event size,
location, and severity, painting interchange as a complex instability sensitive to in-situ
plasma dynamics. Second, an investigation of ionospheric influence on injections
demonstrated limited control, opening up questions on the ionosphere’s role in interchange.
Third, interchange was shown to be adiabatically energizing plasma around
Saturn and long-standing observations of energetic regions of Saturn were explained
through quantification of plasma-neutral interactions. Fourth, the original physics based
classification was used to propose a framework toward applications of machine
learning to gain physical understanding benefiting from the surge of planetary space
physics data available. This work provides a data-rich perspective on mass transport
in planetary magnetospheres through characterizing Saturn’s complex environment
and details a path for integrating physics into machine learning.