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Physics, Instrumentation and Detectors, physics.ins-det, Astrophysics, Instrumentation and Methods for Astrophysics, astro-ph.IM
Abstract:
Suspended optics in gravitational wave (GW) observatories are susceptible to
alignment perturbations and, in particular, to slow drifts over time due to
variations in temperature and seismic levels. Such misalignments affect the
coupling of the incident laser beam into the optical cavities, degrade both
circulating power and optomechanical photon squeezing, and thus decrease the
astrophysical sensitivity to merging binaries. Traditional alignment techniques
involve differential wavefront sensing using multiple quadrant photodiodes, but
are often restricted in bandwidth and are limited by the sensing noise. We
present the first-ever successful implementation of neural network-based
sensing and control at a gravitational wave observatory and demonstrate
low-frequency control of the signal recycling mirror at the GEO 600 detector.
Alignment information for three critical optics is simultaneously extracted
from the interferometric dark port camera images via a CNN-LSTM network
architecture and is then used for MIMO control using soft actor-critic-based
deep reinforcement learning. Overall sensitivity improvement achieved using our
scheme demonstrates deep learning's capabilities as a viable tool for real-time
sensing and control for current and next-generation GW interferometers.
