A Data-Driven Understanding of Plasma Transport in Saturn's Magnetic Environment

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.