Radio-astronomical image reconstruction with a conditional denoising diffusion model

Abstract

Reconstructing sky models from dirty radio images for accurate source extraction, including source localization and flux estimation, is a complex yet critical task, and has important applications in galaxy evolution studies at high redshift, particularly in deep extragalactic fields using for example the Atacama Large Millimetre Array (ALMA). With the development of large-scale projects, such as the Square Kilometre Array (SKA), we anticipate the need for more advanced source-extraction methods. Existing techniques, such as CLEAN and PyBDSF, currently struggle to effectively extract faint sources, highlighting the necessity for the development of more precise and robust methods. The success of the source-extraction process critically depends on the quality and accuracy of image reconstruction. As the imaging process represents an ``information-lossy'' operator, the reconstruction is characterized by uncertainty. The current study proposes the application of stochastic neural networks for the direct reconstruction of sky models from ``dirty'' images. This approach allows us to localize radio sources and to determine their fluxes with corresponding uncertainties, providing a potential advancement in the field of radio-source characterization. We used a dataset of 10164 images simulated with the CASA tool based on the ALMA Cycle 5.3 antenna configuration. We applied conditional denoising diffusion probabilistic models (DDPMs) to directly reconstruct sky models from these dirty images, and then processed these models using Photutils to extract the coordinates and fluxes of the sources. To test the robustness of the proposed model, which was trained on a fixed water vapor value, we examined its performance under varying levels of water vapor. We demonstrate that the proposed approach is state of the art in terms of source localisation, achieving over 90<!PCT!> completeness at a signal-to-noise ratio (S/N) of as low as 2. Additionally, the described method offers an inherent measure of prediction reliability thanks to the stochastic nature of the chosen model. In terms of flux estimation, the proposed model surpasses PyBDSF in terms of performance, accurately extracting fluxes for 96<!PCT!> of the sources in the test set, a notable improvement over the 57<!PCT!> achieved by CLEAN+ PyBDSF. Conditional DDPMs are a powerful tool for image-to-image translation, yielding accurate and robust characterization of radio sources, and outperforming existing methodologies. While this study underscores the significant potential of DDPMs for applications in radio astronomy, we also acknowledge certain limitations that accompany their use, and suggest directions for further refinement and research.