Articles | Volume 42, issue 1
https://doi.org/10.5194/angeo-42-103-2024
https://doi.org/10.5194/angeo-42-103-2024
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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Interactive discussion

Status: closed

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on egusphere-2023-2857', Anonymous Referee #1, 10 Jan 2024
    • AC1: 'Reply on RC1', Noora Partamies, 08 Mar 2024
  • RC2: 'Comment on egusphere-2023-2857', Anonymous Referee #2, 20 Feb 2024
    • AC2: 'Reply on RC2', Noora Partamies, 08 Mar 2024

Peer review completion

AR: Author's response | RR: Referee report | ED: Editor decision | EF: Editorial file upload
ED: Publish subject to minor revisions (review by editor) (08 Mar 2024) by Keisuke Hosokawa
AR by Noora Partamies on behalf of the Authors (11 Mar 2024)  Author's response   Author's tracked changes   Manuscript 
ED: Publish as is (14 Mar 2024) by Keisuke Hosokawa
AR by Noora Partamies on behalf of the Authors (14 Mar 2024)  Author's response   Manuscript 
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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.