Machine Learning Applications to Kronian Magnetospheric Reconnection Classification

Signatures of magnetic reconnection in Saturn's magnetotail are identified in magnetometer observations by characteristic deviations in the northward component of the magnetic field. These magnetic deflections are caused by travelling plasma structures created by reconnection rapidly passing over the observing spacecraft. The identification of these reconnection signatures has 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 machine learning (ML) model to identify evidence of reconnection in Cassini MAG (magnetometer) observations of the Kronian magnetosphere, constructed using the Smith et al, 2016. reconnection catalogue which contains hundreds of examples of plasmoids, travelling compression regions and dipolarizations. This ML model is capable of rapidly identifying reconnection events in large time-span Cassini datasets, tested against the full year of 2010 with a high level of accuracy (99\%), true skill score (0.97), and Heidke skill score (0.85). From this ML model, a full cataloguing and examination of magnetic reconnection in the Kronian magnetosphere across Cassini's near Saturn lifetime is now possible.