Optimal control of wide field small aperture telescope arrays with reinforcement learning

Qiwei Jia; Peng Jia; Jifeng Liu

doi:10.1117/12.2630019

25 August 2022 Optimal control of wide field small aperture telescope arrays with reinforcement learning

Qiwei Jia, Peng Jia, Jifeng Liu

Proceedings Volume 12186, Observatory Operations: Strategies, Processes, and Systems IX; 121860S (2022) https://doi.org/10.1117/12.2630019
Event: SPIE Astronomical Telescopes + Instrumentation, 2022, Montréal, Québec, Canada

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

Citation Download Citation

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

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available