LIGO Document P1800129-v1

Extending the reach of gravitational-wave detectors with machine learning

Document #:
LIGO-P1800129-v1
Document type:
P - Publications
Other Versions:
Abstract:
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.
Files in Document:
Other Files:
Keywords:
SURF18
Notes and Changes:
Change abstract

DCC Version 3.4.2, contact DCC Help