First neural-network sensing and control in a gravitational-wave detector: GEO600 scientists demonstrate first-ever successful implementation
Modern kilometer-sized gravitational-wave detectors such as LIGO, Virgo, KAGRA, and GEO600 are very complex systems that rely on precisely aligned multi-component suspended optics. This alignement must be kept as close as possible to an optimum configuration at all times while being disturbed by environmental influences. Deviations from precise aligment lower the gravitational-wave measurement sensitivity of the detectors. To overcome limitations of current techniques for aligment, researchers at the Max Planck Institute for Gravitational Physics (Albert Einstein Institute) and at Leibniz University Hannover have successfully implemented for the very first time an aligment sensing and control based on neural networks in a gravitational-wave detector. Their demonstration is not only yet another detector technology breakthrough by the GEO600 team, but also a promising first step towards more general machine learning-based control in current and future generation gravitational-wave observatories.