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. We train the DDPM using the FIRST radio galaxy catalog, the Radio Galaxies Zoo and cutouts of the MGCLS catalog. We compared different statistical distributions to make sure that our unconditional approach produces morphologically realistic galaxies, offering a data-driven method to supplement existing radio datasets and support the development of machine learning applications in radio astronomy.