Opto-Electronic Engineering, Volume. 51, Issue 2, 230210(2024)

Reinforcement learning-based stray light suppression study for space-based gravitational wave detection telescope system

Ziyang Zhang1... Jun Chang1,*, Yifan Huang1,**, Qinfang Chen2 and Yunan Wu1 |Show fewer author(s)
Author Affiliations
  • 1School of Optics and Photonics, Beijing Institute of Technology, Beijing 100081, China
  • 2Xi'an Institute of Optics and Precision Mechanics, Chinese Academy of Sciences, Xi’an, Shaanxi 710119, China
  • show less

    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.

    Keywords
    Tools

    Get Citation

    Copy Citation Text

    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

    Download Citation

    EndNote(RIS)BibTexPlain Text
    Save article for my favorites
    Paper Information

    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)

    DOI:10.12086/oee.2024.230210

    Topics