Integration of machine learning and other software solutions to enhance SALT’s observational efficiency

Malcolm C. Scarrott; Janus D. Brink; Encarni R. Colmenero; Anthony R. Koeslag; Lisa A. Crause

doi:10.1117/12.3019605

25 July 2024 Integration of machine learning and other software solutions to enhance SALT’s observational efficiency

Malcolm C. Scarrott, Janus D. Brink, Encarni R. Colmenero, Anthony R. Koeslag, Lisa A. Crause

Proceedings Volume 13101, Software and Cyberinfrastructure for Astronomy VIII; 131013O (2024) https://doi.org/10.1117/12.3019605
Event: SPIE Astronomical Telescopes + Instrumentation, 2024, Yokohama, Japan

Conference Poster

Abstract

The Southern African Large Telescope (SALT) has initiated a SALT Efficiency Project which aims to mitigate instrument overheads by employing software solutions in areas that waste the most time. Efforts were directed into improving the telescope’s complex focus model, the Guider Pre-Positioning (GPP) for all instruments, and the telescope’s pointing. An approximate 50 seconds have been saved for each observation done since November 2023 as a result of the solutions presented. A current project involves developing a new Observational Control System (OCS) to automate repetitive and repeatable observational steps, such as calibrations and instrument offsets. Another current project aims to introduce a Pyramid WaveFront Sensor (PWFS) onto the Fibre Instrument Feed (FIF) guider, allowing it to give focus feedback to the telescope. SALT plans to have both of these solutions implemented by 2025.

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

Citation Download Citation

Malcolm C. Scarrott, Janus D. Brink, Encarni R. Colmenero, Anthony R. Koeslag, and Lisa A. Crause "Integration of machine learning and other software solutions to enhance SALT’s observational efficiency", Proc. SPIE 13101, Software and Cyberinfrastructure for Astronomy VIII, 131013O (25 July 2024); https://doi.org/10.1117/12.3019605

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