The seismically excited motion of a high-Q pendulum in gravitational-wave observatories sets a sensitivity limit to sub-audio gravitational-wave frequencies. Here, we report on the use of machine learning to predict the motion of a high-Q pendulum with a resonance frequency of 1.4 Hz that is driven by natural seismic activity. We achieve a reduction in the displacement power spectral density of 40 dB at the resonant frequency 1.4 Hz and 6 dB at 11 Hz. Our result suggests that machine learning is able to significantly reduce seismically induced test mass motion in gravitational-wave detectors in combination with corrective feed-forward techniques.
Predicting the motion of a high-Q pendulum subject to seismic perturbations using machine learning
Note: This paper is part of the APL Special Collection on Gravitational Wave Detectors.
Nicolas Heimann, Jan Petermann, Daniel Hartwig, Roman Schnabel, Ludwig Mathey; Predicting the motion of a high-Q pendulum subject to seismic perturbations using machine learning. Appl. Phys. Lett. 19 June 2023; 122 (25): 254101. https://doi.org/10.1063/5.0144593
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