LIGO Document P1800129-v1

Extending the reach of gravitational-wave detectors with machine learning

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P - Publications
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We apply Long Short-Term Memory (LSTM) Neural Networks as a time-series regression analysis
technique to filter instrumental noises from gravitational-wave detectors at LIGO. Unlike traditional
neural networks, LSTM networks can store and use information from their past inputs, and thus is
robust in handling sequential data like gravitational-wave signals. Once trained on the detector noise
data, an LSTM network should be able to learn, predict, and subtract both the linear and non-linear
noise coupling mechanisms. This would result in a sensitivity improvement and allow the detection of
gravitational-wave sources currently below the noise floor.
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Associated with Events:
held from 23 Aug 2018 to 24 Aug 2018 in Caltech SCR, West Bridge 351, TeamSpeak

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