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

Mar 08, 2019 - 9:30 am to 10:30 am

SLAC, Kavli 3rd Floor Conf. Room

Claire Hebert

Join us this Friday, March 8th for the latest edition of the Stats & ML Journal club. We will meet at our usual time, 9:30 am on the Kavli Third floor at SLAC, and simultaneously on zoom <https://stanford.zoom.us/j/2038764923> . This week, Claire Hebert will be leading a discussion on a paper using reinforcement learning to optimize survey strategy, which should be very relevant to our interests!




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 controllability of the behavior, adjustability of the mission, and quick 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.