Kavli Affiliate: Chao-Lin Kuo
| First 5 Authors: P. M. Chichura, A. Rahlin, A. J. Anderson, B. Ansarinejad, M. Archipley
| Summary:
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 $2.14”$ in cross-elevation and
$3.57”$ in elevation, well below our goal of $5”$ along each axis. We deploy
our models on the telescope control system and perform further in situ test
observations during the EHT observing campaign in 2024 April. Our models result
in significantly improved pointing accuracy: for sources within the range of
input variables where the models are best trained, average combined pointing
error improved 33%, from $15.9”$ to $10.6”$. These improvements, while
significant, fall shy of our ultimate goal, but they serve as a proof of
concept for the development of future models. Planned upgrades to the EHT
receiver on the SPT will necessitate even stricter pointing accuracy which will
be achievable with our methods.
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