AI 中文总结
该研究针对下一代地基引力波探测器的协同设计问题，提出基于ML、RL与DP的IfoScout方法，经模拟验证可优化探测器灵敏度并降低成本，有望助力新一代探测器设计。
AI 中文摘要
关于下一代地基引力波探测器的设计理念与选址的讨论仍在进行中。在此背景下，我们提出IfoScout，一种基于最先进机器学习（ML）技术的探测器协同设计创新方法。我们对一组虚构的L形干涉仪网络进行两阶段模拟，其灵敏度在物理与地理约束内优化，间接降低了成本。为实现这一点，我们收集两个候选选址的公开数据，通过强化学习（RL）确定干涉仪的长度与朝向；接着，利用差分规划（DP）优化与腔稳定性相关的内部探测器参数，以获得最佳灵敏度。我们认为，若能获取选定站点的地理、地质地图等精确数据，以及干涉仪的详细现实模拟，IfoScout有望对新一代探测器（如爱因斯坦望远镜、宇宙探索者等）的最终设计产生积极影响。
英文摘要
Discussions around the design philosophy and location of the next generation of ground-based gravitational wave detectors are still underway. In this context, we propose IfoScout, an innovative methodology for detector co-design based on state-of-the-art machine-learning (ML) techniques. We present a two-stage simulation of a network of fictional L-shaped interferometers whose sensitivity is optimized within physical and geographical constraints, indirectly resulting in reducing the costs. To achieve this, we gather publicly available data for two token locations and establish the length and orientation with reinforcement learning (RL). Next, we optimize the internal detector parameters related to cavity stability to achieve the best possible sensitivity by means of differential programming (DP). We make the case that IfoScout could have a positive impact on the final design of new generation detectors (e.g. the Einstein Telescope, the Cosmic Explorer, etc.), given precise data (e.g. geographical and geological maps of chosen sites) and detailed, realistic simulations of the interferometers.
Comments7+3 pages, 3+3 figures
