A Guided Unconditional Diffusion Model to Synthesize and Inpaint Radio Galaxies from FIRST, MGCLS and Radio Zoo

Abstract

We present a masked-guided approach for a denoising diffusion probabilistic model (DDPM) trained to generate and inpaint realistic radio galaxy images. The inpainting capability is particularly relevant for reconstructing incomplete observations, improving downstream tasks such as source characterization and morphological classification. We train the model on a combination of the FIRST survey, Radio Galaxy Zoo, and cutouts from the MGCLS survey, enabling it to capture a broad range of radio galaxy morphologies across different observational regimes. We evaluate the realism of the generated samples through statistical comparisons with real data, ensuring consistency in key morphological and intensity distributions. Our unconditional model produces morphologically plausible galaxies while maintaining diversity, highlighting the suitability of diffusion models for this task. This approach provides a scalable alternative to computationally expensive simulations and enables effective data augmentation for machine learning applications in radio astronomy, including source detection, classification, and image reconstruction.