Deep learning solutions to telescope pointing and guiding

TL;DR

This paper presents a novel deep learning-based telescope pointing system that improves precision and efficiency for the WIYN 3.5m Telescope, bypassing traditional pointing models with a recurrent neural network.

Contribution

It introduces a new neural network approach for telescope pointing that does not depend on traditional physical models, enhancing operational accuracy and efficiency.

Findings

Preliminary results show improved pointing accuracy.

The system reduces reliance on physical pointing models.

Plans for generalization to other observatories are discussed.

Abstract

The WIYN 3.5m Telescope at Kitt Peak National Observatory hosts a suite of optical and near infrared instruments, including an extreme precision, optical spectrograph, NEID, built for exoplanet radial velocity studies. In order to achieve sub ms precision, NEID has strict requirements on survey efficiency, stellar image positioning, and guiding performance, which have exceeded the native capabilities of the telescope's original pointing and tracking system. In order to improve the operational efficiency of the telescope we have developed a novel telescope pointing system, built on a recurrent neural network, that does not rely on the usual pointing models (TPoint or other quasi physical bases). We discuss the development of this system, how the intrinsic properties of the pointing problem inform our network design, and show preliminary results from our best models. We also discuss plans for the generalization of this framework, so that it can be applied at other sites.