Radio Galaxy Classification with wGAN-Supported Augmentation
| dc.contributor.author | Kummer,Janis | |
| dc.contributor.author | Rustige,Lennart | |
| dc.contributor.author | Griese,Florian | |
| dc.contributor.author | Borras,Kerstin | |
| dc.contributor.author | Brüggen,Marcus | |
| dc.contributor.author | Connor,Patrick L. S. | |
| dc.contributor.author | Gaede,Frank | |
| dc.contributor.author | Kasieczka,Gregor | |
| dc.contributor.author | Schleper,Peter | |
| dc.contributor.editor | Demmler, Daniel | |
| dc.contributor.editor | Krupka, Daniel | |
| dc.contributor.editor | Federrath, Hannes | |
| dc.date.accessioned | 2022-09-28T17:10:27Z | |
| dc.date.available | 2022-09-28T17:10:27Z | |
| dc.date.issued | 2022 | |
| dc.description.abstract | Novel 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.doi | 10.18420/inf2022_38 | |
| dc.identifier.isbn | 978-3-88579-720-3 | |
| dc.identifier.pissn | 1617-5468 | |
| dc.identifier.uri | https://dl.gi.de/handle/20.500.12116/39537 | |
| dc.language.iso | en | |
| dc.publisher | Gesellschaft für Informatik, Bonn | |
| dc.relation.ispartof | INFORMATIK 2022 | |
| dc.relation.ispartofseries | Lecture Notes in Informatics (LNI) - Proceedings, Volume P-326 | |
| dc.subject | Radio galaxy classification | |
| dc.subject | Generative models | |
| dc.subject | GANplyfication | |
| dc.title | Radio Galaxy Classification with wGAN-Supported Augmentation | en |
| gi.citation.endPage | 478 | |
| gi.citation.startPage | 469 | |
| gi.conference.date | 26.-30. September 2022 | |
| gi.conference.location | Hamburg | |
| gi.conference.sessiontitle | Workshop on Machine Learning for Astroparticle Physics and Astronomy (ml.astro) |
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