Opto-Electronic Engineering, Volume. 51, Issue 2, 230210(2024)
Reinforcement learning-based stray light suppression study for space-based gravitational wave detection telescope system
In gravitational wave telescopes, the energy of the collected space target light signals is dwarfed by the energy of stray light, necessitating robust stray light suppression for reliable telescope operation. Due to the inherent unpredictability of scattered light and the intricate nature of opto-mechanical systems, the formulation of stray light suppression strategies often involves complex mathematical modeling, substantial expertise, and iterative simulations. This paper introduces a Reinforcement Learning-based approach to devise the stray light suppression scheme within a Monte Carlo ray tracing environment, specifically for space gravitational wave telescope systems. Our empirical findings confirm the efficacy of this methodology in generating effective stray light suppression strategies, yielding favorable suppression performance. This study contributes a novel, efficient, and adaptable solution to the stray light challenges faced in space gravitational wave detection as well as other high-precision optical systems, thereby holding extensive applicative promise.
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Ziyang Zhang, Jun Chang, Yifan Huang, Qinfang Chen, Yunan Wu. Reinforcement learning-based stray light suppression study for space-based gravitational wave detection telescope system[J]. Opto-Electronic Engineering, 2024, 51(2): 230210
Category: Article
Received: Aug. 30, 2023
Accepted: Dec. 11, 2023
Published Online: Apr. 26, 2024
The Author Email: Chang Jun (常军), Huang Yifan (黄一帆)