Machine Learning Applications to Kronian Magnetospheric Reconnection Classification

Garton, Tadhg M., Jackman, Caitriona M., Smith, Andy, Yeakel, Kiley L., Maloney, Shane A. and Vandegriff, Jon (2021) Machine Learning Applications to Kronian Magnetospheric Reconnection Classification. Frontiers in Astronomy and Space Sciences, 7. p. 600031. ISSN 2296-987X

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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 traveling 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 coordinates as input. This model is constructed from a catalog of reconnection events which covers three years of observations with a total of 2093 classified events, categorized into plasmoids, traveling 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 cataloging and examination of magnetic reconnection events in the Kronian magnetosphere across Cassini's near Saturn lifetime is now possible.

Item Type: Article
Additional Information: Funding Information: TG’s work is supported by the Science and Technology Facilities Council Opportunities Fund Grant ST/T002255/1. CJ’s work at Southampton was supported by the STFC Ernest Rutherford Fellowship ST/L004399/1. AS was supported by STFC Consolidated Grant ST/S000240/1 and NERC Grant NE/ P017150/1.
Uncontrolled Keywords: machine learning, magnetic reconnection, magnetotail, planetary magnetospheres, plasmoid
Subjects: F300 Physics
F500 Astronomy
Department: Faculties > Engineering and Environment > Mathematics, Physics and Electrical Engineering
Depositing User: Rachel Branson
Date Deposited: 07 Nov 2022 10:48
Last Modified: 07 Nov 2022 11:00

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