Simulating realistic radio continuum survey maps with diffusion models

Abstract

The next generation of radio surveys is going to be transformative for cosmology and other aspects of our understanding of astrophysics. Realistic simulations of radio observations are essential for the design and planning of radio surveys. They are employed in the development of methods for tasks, such as data calibration and reduction, automated analysis and statistical studies in cosmology. We implemented a software for machine learning-assisted simulations of realistic surveys with the LOFAR telescope, resulting in a synthetic radio sky model and a corresponding artificial telescope observation. We employed a diffusion model trained on LoTSS observations to generate individual radio galaxy images with control over the angular size. Single sources are assembled into a radio sky model, using an input catalog from cosmological simulations. We then transformed this sky model into visibilities corresponding to a typical LoTSS pointing. We added realistic noise to this synthetic measurement and obtained our final simulated sky maps through deconvolution. We explored different ways to evaluate our resulting sky model. We were able to simulate realistic LOFAR observations, covering a sky patch of 5x5 degrees at an effective resolution of 8.5 arcseconds. The simulated sources have flux and size distributions that match real observations, and the resulting maps have sensitivities compatible with LoTSS observations. Our diffusion model is able to synthesize high-quality realistic radio galaxy images with precise control over the source sizes. This software can readily be applied to other instruments.