Pointing model meets deep learning: a retrospective study on a MeerKAT+ telescope applying deep learning methods for blind pointing corrections

Stefan Thoms; Matthias Reichert

doi:10.1117/12.3018801

25 July 2024 Pointing model meets deep learning: a retrospective study on a MeerKAT+ telescope applying deep learning methods for blind pointing corrections

Stefan Thoms, Matthias Reichert

Author Affiliations +

Proceedings Volume 13101, Software and Cyberinfrastructure for Astronomy VIII; 131010N (2024) https://doi.org/10.1117/12.3018801
Event: SPIE Astronomical Telescopes + Instrumentation, 2024, Yokohama, Japan

Abstract

This study aims to compare the effectiveness of deep learning methods, specifically Feedforward Neural Networks (FNN), with traditional Pointing Models (PMs) for compensating Blind Pointing Errors in astronomical instruments. Ambitious projects like the ongoing study for the Atacama Large Aperture Submillimeter Telescope (AtLAST† ) inspired to investigate possible improvements of traditional Pointing Error (PE) modeling. The study assesses the practicality of FNNs by applying them to data from an instrument in operation: a precursor MeerKAT+ telescope from the Max Planck Institute for Radio Astronomy (MPIfR) to extend the current MeerKAT Radio Telescope Array at the South African Radio Astronomy Observatory (SARAO) site in the Meerkat National Park in South Africa.

Conference Presentation

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Stefan Thoms and Matthias Reichert "Pointing model meets deep learning: a retrospective study on a MeerKAT+ telescope applying deep learning methods for blind pointing corrections", Proc. SPIE 13101, Software and Cyberinfrastructure for Astronomy VIII, 131010N (25 July 2024); https://doi.org/10.1117/12.3018801

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