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Suspended optics in gravitational wave (GW) observatories are susceptible to
alignment perturbations, particularly 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.
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