Morphological Classification of Radio Galaxies with wGAN-supported Augmentation

TL;DR

This paper explores using Wasserstein GANs to generate synthetic radio galaxy images, augment training data, and improve the accuracy of deep learning models for morphological classification, especially benefiting simpler architectures.

Contribution

It introduces a GAN-based data augmentation approach to enhance supervised deep learning models for radio galaxy classification, demonstrating its effectiveness across different architectures.

Findings

FCN models show significant accuracy improvement with GAN-generated data.

Complex classifiers like CNNs see slight improvements.

Vision Transformers do not benefit from GAN augmentation.

Abstract

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 for the forthcoming large radio surveys. We demonstrate the use of generative models, specifically Wasserstein GANs (wGANs), to generate data for different classes of radio galaxies. Further, we study the impact of augmenting the training data with images from our wGAN on three different classification architectures. We find that this technique makes it possible to improve models for the morphological classification of radio galaxies. A simple Fully Connected Neural Network (FCN) benefits most from including generated images into the training set, with a considerable improvement of its classification accuracy. In addition, we find it is more difficult to improve complex classifiers. The classification performance of a Convolutional Neural Network (CNN) can be improved slightly. However, this is not the case for a Vision Transformer (ViT).