基于强化学习的空间引力波探测望远镜系统外杂光抑制研究

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

引力波望远镜其收集的空间目标光信号能量远小于杂散光能量,为了保证引力波望远镜的正常工作,需要保证较好的杂光抑制效果。又由于散射光线的不确定性,光机系统本身的复杂性,杂光抑制方案的确定往往需要复杂的数学公式计算以及丰富的经验与充足时间进行仿真迭代。本文提出了一种基于强化学习的杂光抑制策略,针对空间引力波探测望远镜系统中的环境杂光问题,采用蒙特卡洛光线追迹方法进行分析和处理。通过制定有效的杂光抑制措施,实现了对该系统中杂光的有效控制。仿真结果验证了该方法在空间引力波探测望远镜系统中杂光抑制方面的优异性能,展示了其具有良好杂光抑制效果的潜力。这一研究为解决空间引力波探测和其他高精度光学系统中的杂光问题提供了一种高效、灵活的新方法,具有广泛的应用潜力。

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 highprecision optical systems,thereby holding extensive applicative promise.