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...

Full description

Saved in:
Bibliographic Details
Published inApplied physics letters Vol. 122; no. 25
Main Authors Heimann, Nicolas, Petermann, Jan, Hartwig, Daniel, Schnabel, Roman, Mathey, Ludwig
Format Journal Article
LanguageEnglish
Published Melville American Institute of Physics 19.06.2023
Subjects
Applied physics
Gravitational waves
Machine learning
Observatories
Pendulums
Perturbation
Power spectral density
Resonant frequencies
Seismic activity
Online AccessGet full text

Cover

More Information
Summary: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.
Bibliography:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 14
ISSN:0003-6951
1077-3118
DOI:10.1063/5.0144593