Machine Learning Applications to Kronian Magnetospheric Reconnection Classification
Tadhg M. Garton, Caitriona M. Jackman, Andy W. Smith, Kiley L. Yeakel,, Shane A. Maloney, Jon Vandegriff

TL;DR
This paper introduces a fully automated neural network model that accurately identifies magnetic reconnection events in Saturn's magnetosphere using Cassini data, enabling large-scale analysis of these phenomena.
Contribution
The study presents the first supervised learning model for automatic detection of reconnection events in Saturn's magnetosphere, reducing reliance on manual identification methods.
Findings
Achieved 87% accuracy in identifying reconnection events
Successfully classified 2093 events into three categories
Enabled comprehensive cataloging of reconnection in Saturn's magnetosphere
Abstract
The products of magnetic reconnection in Saturn's magnetotail are identified in magnetometer observations primarily through characteristic deviations in the north-south component of the magnetic field. These magnetic deflections are caused by travelling plasma structures created during reconnection rapidly passing over the observing spacecraft. Identification of these signatures have long been performed by eye, and more recently through semi-automated methods, however these methods are often limited through a required human verification step. Here, we present a fully automated, supervised learning, feed forward neural network model to identify evidence of reconnection in the Kronian magnetosphere with the three magnetic field components observed by the Cassini spacecraft in Kronocentric radial-theta-phi (KRTP) coordinates as input. This model is constructed from a catalogue of…
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