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Proceedings Paper

Feature-based telescope scheduler
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Paper Abstract

Feature-based Scheduler offers a sequencing strategy for ground-based telescopes. This scheduler is designed in the framework of Markovian Decision Process (MDP), and consists of a sub-linear online controller, and an offline supervisory control-optimizer. Online control law is computed at the moment of decision for the next visit, and the supervisory optimizer trains the controller by simulation data. Choice of the Differential Evolution (DE) optimizer, and introducing a reduced state space of the telescope system, offer an efficient and parallelizable optimization algorithm. In this study, we applied the proposed scheduler to the problem of Large Synoptic Survey Telescope (LSST). Preliminary results for a simplified model of LSST is promising in terms of both optimality, and computational cost.

Paper Details

Date Published: 15 July 2016
PDF: 10 pages
Proc. SPIE 9910, Observatory Operations: Strategies, Processes, and Systems VI, 991011 (15 July 2016); doi: 10.1117/12.2232053
Show Author Affiliations
Elahesadat Naghib, Princeton Univ. (United States)
Robert J. Vanderbei, Princeton Univ. (United States)
Christopher Stubbs, Harvard Univ. (United States)

Published in SPIE Proceedings Vol. 9910:
Observatory Operations: Strategies, Processes, and Systems VI
Alison B. Peck; Robert L. Seaman; Chris R. Benn, Editor(s)

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