Co-design of ground-based gravitational wave detector networks

Abstract

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.