[2505.09365] ARCANE -- Early Detection of Interplanetary Coronal Mass Ejections

[2505.09365] ARCANE -- Early Detection of Interplanetary Coronal Mass Ejections

arXiv - Machine Learning 4 min read

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Abstract page for arXiv paper 2505.09365: ARCANE -- Early Detection of Interplanetary Coronal Mass Ejections

Physics > Space Physics arXiv:2505.09365 (physics) [Submitted on 14 May 2025 (v1), last revised 2 Mar 2026 (this version, v3)] Title:ARCANE -- Early Detection of Interplanetary Coronal Mass Ejections Authors:H. T. Rüdisser, G. Nguyen, J. Le Louëdec, E. E. Davies, C. Möstl View a PDF of the paper titled ARCANE -- Early Detection of Interplanetary Coronal Mass Ejections, by H. T. R\"udisser and 4 other authors View PDF HTML (experimental) Abstract:Interplanetary coronal mass ejections (ICMEs) are major drivers of space weather disturbances, posing risks to both technological infrastructure and human activities. Automatic detection of ICMEs in solar wind in situ data is essential for early warning systems. While several methods have been proposed to identify these structures in time series data, robust real-time detection remains a significant challenge. In this work, we present ARCANE - the first framework explicitly designed for early ICME detection in streaming solar wind data under realistic operational constraints, enabling event identification without requiring observation of the full structure. Our approach evaluates the strengths and limitations of detection models by comparing a machine learning-based method to a threshold-based baseline. The ResUNet++ model, previously validated on science data, significantly outperforms the baseline, particularly in detecting high-impact events, while retaining solid performance on lower-impact cases. Notably, we find that using re...

Originally published on March 03, 2026. Curated by AI News.

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