  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 reconnection events which covers three
years of observations with a total of 2093 classified events, categorized into
plasmoids, travelling compression regions and dipolarizations. This neural
network model is capable of rapidly identifying reconnection events in large
time-span Cassini datasets, tested against the full year 2010 with a high level
of accuracy (87%), true skill score (0.76), and Heidke skill score (0.73). From
this model, a full cataloguing and examination of magnetic reconnection events
in the Kronian magnetosphere across Cassini's near Saturn lifetime is now
possible.
