Morphological classification of radio galaxies with Wasserstein generative adversarial network-supported augmentation

Machine learning techniques that perform morphological classification of astronomical sources often suffer from a scarcity of labelled training data. Here, we focus on the case of supervised deep learning models for the morphological classification of radio galaxies, which is particularly topical fo...

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Veröffentlicht in:	RAS techniques and instruments 2023-01, Vol.2 (1), p.264-277
Hauptverfasser:	Rustige, Lennart, Kummer, Janis, Griese, Florian, Borras, Kerstin, Bruggen, Marcus, Connor, Patrick L.S, Gaede, Frank, Kasieczka, Gregor, Knopp, Tobias, Schleper, Peter
Format:	Artikel
Sprache:	eng
Schlagworte:	Astronomical research Machine learning Neural networks
Online-Zugang:	Volltext
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