Radio Telescope

Radio telescopes generate massive datasets requiring advanced signal processing to create high-quality images of celestial objects. Current research focuses on improving image reconstruction techniques, employing deep learning models like convolutional neural networks and diffusion probabilistic models to overcome challenges such as noise, sparsity, and artifacts in the raw data. These advancements are crucial for analyzing the increasingly large datasets from next-generation telescopes, enabling more efficient detection and classification of astronomical phenomena, such as radio transients and solar bursts. The improved image quality facilitates more accurate scientific analysis and discovery.