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AKPIK: Arbeitskreis Physik, moderne Informationstechnologie und Künstliche Intelligenz

AKPIK 4: Deep Learning

AKPIK 4.3: Vortrag

Donnerstag, 24. März 2022, 16:45–17:00, AKPIK-H13

Deep Learning-based Imaging in Radio Interferometry — •Felix Geyer and Kevin Schmidt — Astroparticle Physics AG Elsässer, TU Dortmund University, Germany

Radio interferometry is used to monitor and observe distant astronomical sources and objects with high resolution. Especially Very Long Baseline Interferometry allows achieving the highest resolutions by combining the data of multiple telescopes. This results in an effective diameter corresponding to the greatest distance between two telescopes. The taken data consists of visibilities, which depend on the baselines between the telescopes. Because the distribution of these baselines is sparse, the sample of visibilities is incomplete. After transforming this sample to spatial space, this so-called "dirty image" is inadequate for physical analyses. Thus, the image undergoes an elongated and mostly manually performed cleaning process in order to remove background artifacts and to restore the original source distribution. We developed a new and fast approach to reconstruct missing data reasonably using Neural Networks. This talk will present the current state of the radionets framework and an outlook on upcoming projects. One focus will be on the simulation improvements using the RIME formalism.