LIGO Document T1900359-v1

Extending the Reach of Gravitational-wave Detectors with Machine Learning

Document #:
LIGO-T1900359-v1
Document type:
T - Technical notes
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Abstract:
This proposal presents the idea of using current machine learning techniques and algorithms to reduce the overall noise floor of the LIGO detectors. There will be a hard emphasis on techniques that analyze time series data, such as utilizing long short-term memory and nonlinear regression algorithms. While other sources of noises in the detectors are outlined in the proposal, there will be a focus on using machine learning algorithms to hone in on noise sources coming from the physical attributes of the instrument itself. The goal is to increase the sensitivity of the detectors by subtracting linear and non-linear noise coupling mechanisms.
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Keywords:
SURF19
Notes and Changes:
Morgan Nanez SURF19 project material
Associated with Events:
held from 22 Aug 2019 to 23 Aug 2019 in Caltech SCR, West Bridge 351, TeamSpeak

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