Pointing Accuracy Improvements for the South Pole Telescope with Machine Learning
P. M. Chichura, A. Rahlin, A. J. Anderson, B. Ansarinejad, M. Archipley, L. Balkenhol, K. Benabed, A. N. Bender, B. A. Benson, F. Bianchini, L. E. Bleem, F. R. Bouchet, L. Bryant, E. Camphuis, J. E. Carlstrom, C. L. Chang, P. Chaubal, A. Chokshi, T.-L. Chou, A. Coerver

TL;DR
This paper demonstrates how machine learning models trained on historical data can significantly improve the pointing accuracy of the South Pole Telescope, especially during critical EHT observations.
Contribution
The authors develop and deploy XGBoost models to correct telescope pointing errors based on weather data, achieving substantial accuracy improvements.
Findings
Pointing error reduced by 33% during EHT campaign
Models achieved RMS errors below 4'' in tests
Proof of concept for future accuracy enhancements
Abstract
We present improvements to the pointing accuracy of the South Pole Telescope (SPT) using machine learning. The ability of the SPT to point accurately at the sky is limited by its structural imperfections, which are impacted by the extreme weather at the South Pole. Pointing accuracy is particularly important during SPT participation in observing campaigns with the Event Horizon Telescope (EHT), which requires stricter accuracy than typical observations with the SPT. We compile a training dataset of historical observations of astronomical sources made with the SPT-3G and EHT receivers on the SPT. We train two XGBoost models to learn a mapping from current weather conditions to two telescope drive control arguments -- one which corrects for errors in azimuth and the other for errors in elevation. Our trained models achieve root mean squared errors on withheld test data of in…
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Taxonomy
TopicsAdaptive optics and wavefront sensing · Astronomy and Astrophysical Research · Inertial Sensor and Navigation
