TY - JOUR
T1 - Machine Learning Applications to Kronian Magnetospheric Reconnection Classification
AU - Garton, Tadhg M.
AU - Jackman, Caitriona M.
AU - Smith, Andrew W.
AU - Yeakel, Kiley L.
AU - Maloney, Shane A.
AU - Vandegriff, Jon
N1 - 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.
PY - 2021/3/10
Y1 - 2021/3/10
N2 - 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.
AB - 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.
KW - machine learning
KW - magnetic reconnection
KW - magnetotail
KW - planetary magnetospheres
KW - plasmoid
UR - http://www.scopus.com/inward/record.url?scp=85113751574&partnerID=8YFLogxK
U2 - 10.3389/fspas.2020.600031
DO - 10.3389/fspas.2020.600031
M3 - Article
AN - SCOPUS:85113751574
SN - 2296-987X
VL - 7
JO - Frontiers in Astronomy and Space Sciences
JF - Frontiers in Astronomy and Space Sciences
M1 - 600031
ER -