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  Identification of Coronal Holes on AIA/SDO Images Using Unsupervised Machine Learning

Inceoglu, F., SHPRITS, Y., Heinemann, S. G., Bianco, S. (2022): Identification of Coronal Holes on AIA/SDO Images Using Unsupervised Machine Learning. - The Astrophysical Journal, 930, 2, 118.
https://doi.org/10.3847/1538-4357/ac5f43

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Inceoglu, Fadil1, Autor              
SHPRITS, YURI1, Autor              
Heinemann, Stephan G.2, Autor
Bianco, Stefano1, Autor              
Affiliations:
12.7 Space Physics and Space Weather, 2.0 Geophysics, Departments, GFZ Publication Database, Deutsches GeoForschungsZentrum, ou_2239888              
2External Organizations, ou_persistent22              

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 Zusammenfassung: Through its magnetic activity, the Sun governs the conditions in Earth's vicinity, creating space weather events, which have drastic effects on our space- and ground-based technology. One of the most important solar magnetic features creating the space weather is the solar wind that originates from the coronal holes (CHs). The identification of the CHs on the Sun as one of the source regions of the solar wind is therefore crucial to achieve predictive capabilities. In this study, we used an unsupervised machine-learning method, k-means, to pixel-wise cluster the passband images of the Sun taken by the Atmospheric Imaging Assembly on the Solar Dynamics Observatory in 171, 193, and 211 Å in different combinations. Our results show that the pixel-wise k-means clustering together with systematic pre- and postprocessing steps provides compatible results with those from complex methods, such as convolutional neural networks. More importantly, our study shows that there is a need for a CH database where a consensus about the CH boundaries is reached by observers independently. This database then can be used as the "ground truth," when using a supervised method or just to evaluate the goodness of the models.

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Sprache(n): eng - Englisch
 Datum: 2022-05-102022
 Publikationsstatus: Final veröffentlicht
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 Identifikatoren: DOI: 10.3847/1538-4357/ac5f43
GFZPOF: p4 T3 Restless Earth
OATYPE: Gold Open Access
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Titel: The Astrophysical Journal
Genre der Quelle: Zeitschrift, SCI, Scopus, oa, ab 2022 Gold OA
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Seiten: - Band / Heft: 930 (2) Artikelnummer: 118 Start- / Endseite: - Identifikator: CoNE: https://gfzpublic.gfz-potsdam.de/cone/journals/resource/journals474
Publisher: IOP Publishing