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Radio Galaxy Classification with wGAN-Supported Augmentation

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dc.contributor.authorKummer,Janis
dc.contributor.authorRustige,Lennart
dc.contributor.authorGriese,Florian
dc.contributor.authorBorras,Kerstin
dc.contributor.authorBrüggen,Marcus
dc.contributor.authorConnor,Patrick L. S.
dc.contributor.authorGaede,Frank
dc.contributor.authorKasieczka,Gregor
dc.contributor.authorSchleper,Peter
dc.contributor.editorDemmler, Daniel
dc.contributor.editorKrupka, Daniel
dc.contributor.editorFederrath, Hannes
dc.date.accessioned2022-09-28T17:10:27Z
dc.date.available2022-09-28T17:10:27Z
dc.date.issued2022
dc.description.abstractNovel techniques are indispensable to process the flood of data from the new generation of radio telescopes. In particular, the classification of astronomical sources in images is challenging. Morphological classification of radio galaxies could be automated with deep learning models that require large sets of labelled training data. Here, we demonstrate the use of generative models, specifically Wasserstein GANs (wGAN), to generate artificial data for different classes of radio galaxies. Subsequently, we augment the training data with images from our wGAN. We find that a simple fully-connected neural network for classification can be improved significantly by including generated images into the training set.en
dc.identifier.doi10.18420/inf2022_38
dc.identifier.isbn978-3-88579-720-3
dc.identifier.pissn1617-5468
dc.identifier.urihttps://dl.gi.de/handle/20.500.12116/39537
dc.language.isoen
dc.publisherGesellschaft für Informatik, Bonn
dc.relation.ispartofINFORMATIK 2022
dc.relation.ispartofseriesLecture Notes in Informatics (LNI) - Proceedings, Volume P-326
dc.subjectRadio galaxy classification
dc.subjectGenerative models
dc.subjectGANplyfication
dc.titleRadio Galaxy Classification with wGAN-Supported Augmentationen
gi.citation.endPage478
gi.citation.startPage469
gi.conference.date26.-30. September 2022
gi.conference.locationHamburg
gi.conference.sessiontitleWorkshop on Machine Learning for Astroparticle Physics and Astronomy (ml.astro)

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P326 - INFORMATIK 2022 - Informatik in den Naturwissenschaften