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SMuK 2023 – wissenschaftliches Programm

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AKPIK: Arbeitskreis Physik, moderne Informationstechnologie und Künstliche Intelligenz

AKPIK 4: Neural Networks II

AKPIK 4.1: Vortrag

Mittwoch, 22. März 2023, 15:45–16:00, ZEU/0118

Morphological Classification of Radio Galaxies with wGAN-supported Augmentation — •Janis Kummer1,3, Florian Griese1,4,5, Lennart Rustige1,2, Kerstin Borras2,6, Marcus Brüggen3, Patrick Connor1,7, Frank Gaede2, Gregor Kasieczka7, Tobias Knopp4,5, and Peter Schleper71Center for Data and Computing in Natural Sciences (CDCS), Hamburg, German — 2Deutsches Elektronen-Synchrotron DESY, Hamburg, German — 3Hamburger Sternwarte, Hamburg, Germany — 4University Medical Center Hamburg-Eppendorf, Hamburg, Germany — 5Hamburg University of Technology,Hamburg, Germany — 6RWTH Aachen University, Aachen, Germany — 7Universität Hamburg, Hamburg, German

Supervised deep learning models for the morphological classification of radio galaxies are very important for processing the data of future large radio surveys. However, labelled training data for such models is limited. We demonstrate the use of generative models, specifically a Wasserstein Generative Adversarial Network (wGAN), to generate artificial data for different classes of radio galaxies. Further, we study the impact of augmenting the training data with images from our wGAN on different classification architectures. We find that it is indeed possible to improve models for the morphological classification of radio galaxies with this technique. In addition, fast simulations of radio galaxies with our wGAN are useful to validate new interferometric machine-learning algorithms.

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