Annales Geophysicae
Annales Geophysicae
ANGEO
Articles | Volume 42, issue 1
Articles | Volume 42, issue 1
https://doi.org/10.5194/angeo-42-103-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/angeo-42-103-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
Articles | Volume 42, issue 1
Regular paper
 | 
25 Apr 2024
Regular paper |  | 25 Apr 2024

Auroral breakup detection in all-sky images by unsupervised learning

Noora Partamies, Bas Dol, Vincent Teissier, Liisa Juusola, Mikko Syrjäsuo, and Hjalmar Mulders

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Cited articles

Akasofu, S.-I.: The development of the auroral substorm, Planet. Space Sci., 4, 273–282, https://doi.org/10.1016/0032-0633(64)90151-5, 1964. a
Clausen, L. B. N. and Nickisch, H.: Automatic classification of auroral images from the Oslo Auroral THEMIS (OATH) data set using machine learning, J. Geophys. Res.-Space, 123, 5640–5647, https://doi.org/10.1029/2018JA025274, 2018. a, b
Cresswell-Moorcock, K., Rodger, C. J., Kero, A., Collier, A. B., Clilverd, M. A., Häggström, I., and Pitkänen, T.: A reexamination of latitudinal limits of substorm-produced energetic electron precipitation, J. Geophys. Res.-Space, 118, 6694–6705, https://doi.org/10.1002/jgra.50598, 2013. a
Dol, B.: Viability of using images classified by an unsupervised AI for determining patterns in the evolution of auroral morphology, Internship report at The University Centre in Svalbard, Norway, Eindhoven University of Technology, the Netherlands, https://bibsys-almaprimo.hosted.exlibrisgroup.com/primo-explore/fulldisplay?docid=BIBSYS_ILS71681826100002201&vid=UNIS&search_scope=default_scope&tab=default_tab&lang=en_US&context=L (last access: 19 April 2024), 2023. a, b, c
Dreyer, J., Partamies, N., Whiter, D., Ellingsen, P. G., Baddeley, L., and Buchert, S. C.: Characteristics of fragmented aurora-like emissions (FAEs) observed on Svalbard, Ann. Geophys., 39, 277–288, https://doi.org/10.5194/angeo-39-277-2021, 2021. a
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Short summary
Auroral imaging produces large amounts of image data that can no longer be analyzed by visual inspection. Thus, every step towards automatic analysis tools is crucial. Previously supervised learning methods have been used in auroral physics, with a human expert providing ground truth. However, this ground truth is debatable. We present an unsupervised learning method, which shows promising results in detecting auroral breakups in the all-sky image data.
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