First demonstration of neural sensing and control in a kilometer-scale gravitational wave observatory

Abstract

Suspended optics in gravitational wave (GW) observatories are susceptible toalignment perturbations and, in particular, to slow drifts over time due tovariations in temperature and seismic levels. Such misalignments affect thecoupling of the incident laser beam into the optical cavities, degrade bothcirculating power and optomechanical photon squeezing, and thus decrease theastrophysical sensitivity to merging binaries. Traditional alignment techniquesinvolve differential wavefront sensing using multiple quadrant photodiodes, butare often restricted in bandwidth and are limited by the sensing noise. Wepresent the first-ever successful implementation of neural network-basedsensing and control at a gravitational wave observatory and demonstratelow-frequency control of the signal recycling mirror at the GEO 600 detector.Alignment information for three critical optics is simultaneously extractedfrom the interferometric dark port camera images via a CNN-LSTM networkarchitecture and is then used for MIMO control using soft actor-critic-baseddeep reinforcement learning. Overall sensitivity improvement achieved using ourscheme demonstrates deep learning's capabilities as a viable tool for real-timesensing and control for current and next-generation GW interferometers.<br