A Framework for Telescope Schedulers: With Applications to the Large Synoptic Survey Telescope

TL;DR

This paper introduces a flexible, automated telescope scheduling framework that improves efficiency and adaptability for large ground-based telescopes, demonstrated through applications to the LSST.

Contribution

The paper presents a generic, feature-based scheduler framework that requires minimal manual tuning and can be adapted to various astronomical telescopes, including LSST.

Findings

The scheduler offers controllability, adjustability, and recoverability.

The LSST-specific scheduler outperforms previous simulation models.

The framework is adaptable to different telescope and survey requirements.

Abstract

How ground-based telescopes schedule their observations in response to competing science priorities and constraints, variations in the weather, and the visibility of a particular part of the sky can significantly impact their efficiency. In this paper we introduce the Feature-Based telescope scheduler that is an automated, proposal-free decision making algorithm that offers \textit{controllability} of the behavior, \textit{adjustability} of the mission, and quick \textit{recoverability} from interruptions for large ground-based telescopes. By framing this scheduler in the context of a coherent mathematical model the functionality and performance of the algorithm is simple to interpret and adapt to a broad range of astronomical applications. This paper presents a generic version of the Feature-Based scheduler, with minimal manual tailoring, to demonstrate its potential and flexibility as a foundation for large ground-based telescope schedulers which can later be adjusted for other instruments. In addition, a modified version of the Feature-Based scheduler for the Large Synoptic Survey Telescope (LSST) is introduced and compared to previous LSST scheduler simulations.