Machine Learning Applications to Kronian Magnetospheric Reconnection Classification
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 r...
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doaj-d19e7f3b19d5483dad9137384b53c7e02021-03-10T05:08:01ZengFrontiers Media S.A.Frontiers in Astronomy and Space Sciences2296-987X2021-03-01710.3389/fspas.2020.600031600031Machine Learning Applications to Kronian Magnetospheric Reconnection ClassificationTadhg M. Garton0Caitriona M. Jackman1Caitriona M. Jackman2Andrew W. Smith3Kiley L. Yeakel4Shane A. Maloney5Jon Vandegriff6Department of Physics and Astronomy, Space Environment Physics Group, University of Southampton, Southampton, EnglandDepartment of Physics and Astronomy, Space Environment Physics Group, University of Southampton, Southampton, EnglandSchool of Cosmic Physics, Dublin Institute for Advanced Studies, Dublin, IrelandMullard Space Science Laboratory, Department of Space and Climate Physics, University College London, London, EnglandJohns Hopkins University Applied Physics Laboratory, Laurel, MD, United StatesSchool of Cosmic Physics, Dublin Institute for Advanced Studies, Dublin, IrelandJohns Hopkins University Applied Physics Laboratory, Laurel, MD, United StatesThe 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.https://www.frontiersin.org/articles/10.3389/fspas.2020.600031/fullmachine learningmagnetic reconnectionplanetary magnetospheresmagnetotailplasmoid |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Tadhg M. Garton Caitriona M. Jackman Caitriona M. Jackman Andrew W. Smith Kiley L. Yeakel Shane A. Maloney Jon Vandegriff |
spellingShingle |
Tadhg M. Garton Caitriona M. Jackman Caitriona M. Jackman Andrew W. Smith Kiley L. Yeakel Shane A. Maloney Jon Vandegriff Machine Learning Applications to Kronian Magnetospheric Reconnection Classification Frontiers in Astronomy and Space Sciences machine learning magnetic reconnection planetary magnetospheres magnetotail plasmoid |
author_facet |
Tadhg M. Garton Caitriona M. Jackman Caitriona M. Jackman Andrew W. Smith Kiley L. Yeakel Shane A. Maloney Jon Vandegriff |
author_sort |
Tadhg M. Garton |
title |
Machine Learning Applications to Kronian Magnetospheric Reconnection Classification |
title_short |
Machine Learning Applications to Kronian Magnetospheric Reconnection Classification |
title_full |
Machine Learning Applications to Kronian Magnetospheric Reconnection Classification |
title_fullStr |
Machine Learning Applications to Kronian Magnetospheric Reconnection Classification |
title_full_unstemmed |
Machine Learning Applications to Kronian Magnetospheric Reconnection Classification |
title_sort |
machine learning applications to kronian magnetospheric reconnection classification |
publisher |
Frontiers Media S.A. |
series |
Frontiers in Astronomy and Space Sciences |
issn |
2296-987X |
publishDate |
2021-03-01 |
description |
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. |
topic |
machine learning magnetic reconnection planetary magnetospheres magnetotail plasmoid |
url |
https://www.frontiersin.org/articles/10.3389/fspas.2020.600031/full |
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