VisRec: A Semi-Supervised Approach to Radio Interferometric Data Reconstruction

arXiv:2403.00897v1 Announce Type: cross
Abstract: Radio telescopes produce visibility data about celestial objects, but these data are sparse and noisy. As a result, images created on raw visibility data are of low quality. Recent studies have used deep learning models to reconstruct visibility data to get cleaner images. However, these methods rely on a substantial amount of labeled training data, which requires significant labeling effort from radio astronomers. Addressing this challenge, we propose VisRec, a model-agnostic semi-supervised learning approach to …

abstract arxiv astro-ph.ga celestial cs.ai cs.cv cs.lg data deep learning eess.iv images low objects quality radio raw semi-supervised studies telescopes type visibility