Jonathan KERN (DEDIP — CEA)

Données manquantes en cosmologie

Resume by AI: This presentation explores efficient learned deconvolution of radio interferometric images using deep unrolling techniques. With the construction of large observatories like SKA generating massive datasets, traditional image reconstruction methods face computational challenges. The presentation introduces algorithm unrolling, which transforms iterative reconstruction methods into deep neural networks, significantly improving speed and performance. It also discusses the CARB (Conformalized Augmented Radio Bootstrap) method for uncertainty quantification, providing reliable confidence intervals without requiring ground truth data. The results demonstrate that unrolling achieves high-quality reconstructions with reduced computational costs, making it a promising approach for large-scale radio astronomy imaging.