A diffusion-based generative augmentation pipeline enables a small-data neural classifier to detect diffuse radio halos in MWA/GLEAM images, rediscovering known halos and proposing new candidates.
Radio Galaxy Classification with wGAN-Supported Augmentation
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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.
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astro-ph.GA 1years
2024 1verdicts
CONDITIONAL 1representative citing papers
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Radio Halo Detection in MWA Data using Deep Neural Networks and Generative Data Augmentation
A diffusion-based generative augmentation pipeline enables a small-data neural classifier to detect diffuse radio halos in MWA/GLEAM images, rediscovering known halos and proposing new candidates.