Presentation + Paper
25 August 2022 Optimal control of wide field small aperture telescope arrays with reinforcement learning
Author Affiliations +
Abstract
In recent years, time domain astronomy has become an active research area. Thanks to its low cost and moderate observation ability, wide field small aperture telescopes are commonly used to observe celestial objects for time domain astronomy. We would use several wide field small aperture telescopes to form an array to observe celestial objects continuously. Because there are many celestial objects for telescope arrays to observe, such as obtaining positions or magnitudes of celestial objects or discovering new transients, it would be necessary to investigate an optimal control strategy to maximize their scientific outputs. To achieve this target, we need to make trade-offs between observations of different targets and define appropriate tasks for each telescope. In this paper, we propose a framework, which includes a simulator and a reinforcement learning based algorithm, to obtain optimal control strategy for wide field small aperture telescope arrays, according to predefined scientific requirements. Our method could achieve better performance than ordinary sky survey strategies and has good generalization ability after training.
Conference Presentation
© (2022) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Qiwei Jia, Peng Jia, and Jifeng Liu "Optimal control of wide field small aperture telescope arrays with reinforcement learning", Proc. SPIE 12186, Observatory Operations: Strategies, Processes, and Systems IX, 121860S (25 August 2022); https://doi.org/10.1117/12.2630019
Advertisement
Advertisement
RIGHTS & PERMISSIONS
Get copyright permission  Get copyright permission on Copyright Marketplace
KEYWORDS
Telescopes

Monte Carlo methods

Computer simulations

Device simulation

Electromagnetic simulation

Algorithm development

Astronomy

Back to Top