Predicting the motion of a high-Q pendulum subject to seismic perturbations using machine learning

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...

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Veröffentlicht in:Applied physics letters 2023-06, Vol.122 (25)
Hauptverfasser: Heimann, Nicolas, Petermann, Jan, Hartwig, Daniel, Schnabel, Roman, Mathey, Ludwig
Format: Artikel
Sprache:eng
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Zusammenfassung: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.
ISSN:0003-6951
1077-3118
DOI:10.1063/5.0144593