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
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
    Figures & Tables(13)
    The optical path diagram of space gravitational wave detection telescope system
    The process of traditional stray light analysis and suppression
    The length diagram of the baffle at different angles
    The optomechanical structure under the traditional design method
    RL network model. (a) Env, Actor, Critic; (b) Ray tracing model for environment
    The reward fuction
    The initial stray light suppression effect of the model
    The stray light suppression effect of the traditional stray light suppression scheme
    The stray light path under the traditional stray light suppression scheme
    The RL running results
    The optical-mechanical structure of RL stray light suppression scheme
    Comparison of the stray light suppression effects of different stray light suppression schemes
    • Table 1. The reflective optical system

      View table
      View in Article

      Table 1. The reflective optical system

      表面曲率厚度/mm材料二次系数Zernike 4Zernike 5
      二次曲面1298.5591625.7814Mirror−1
      二次曲面−49.105547.872Mirror−1.1871
      Zernike 自由曲面Inf−92.4756Mirror−0.0024−0.0497
      平面镜Inf270Mirror
    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: Jun Chang (常军), Yifan Huang (黄一帆)

    DOI:10.12086/oee.2024.230210

    Topics