Next-generation radio observatories (e.g., ngVLA and SKA) will produce massive volumes of data, challenging traditional imaging and source-extraction methods in radio astronomy. We present two complementary AI-driven approaches to address this: (i) a deep neural network framework for direct source localization from radio visibilities, bypassing image reconstruction entirely; and (ii) conditional denoising diffusion models (DDPMs) for reconstructing sky models from dirty radio maps, enabling accurate source localization and flux estimation with inherent uncertainty. On ALMA-simulated datasets, both methods outperform traditional pipelines, particularly in low signal-to-noise regimes, and achieve up to 30× faster execution. These results highlight the potential of stochastic and deep neural networks to deliver robust and efficient solutions for next-generation interferometric imaging.

Speaker: Omkar Bait is a CosmicAI Fellow at the National Radio Astronomy Observatory in Charlottesville, working at the interface of artificial intelligence and radio astronomy. His research focuses on developing novel AI/ML techniques designed to address the data-processing challenges posed by next-generation radio telescopes.