Radio Interferometric

Radio interferometry combines signals from multiple telescopes to create high-resolution images of celestial objects from sparse and noisy data. Current research heavily focuses on improving image reconstruction using advanced algorithms, including deep learning models like denoising diffusion probabilistic models and transformer networks, often incorporating Bayesian frameworks for uncertainty quantification and semi-supervised learning to address data limitations. These advancements are crucial for maximizing the scientific output of next-generation radio telescopes like the Square Kilometre Array, enabling more accurate source detection, characterization, and improved understanding of phenomena across a wide range of astrophysical scales.