Machine Learning Reduces LIGO Noise

This is a news story, published by Phys Org, that relates primarily to LIGO news.

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LIGO detector sites

Phys Org

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Science

Machine learning approach can enhance observatory's hunt for gravitational waves

Summary

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89% Informative

University of California, Riverside developed machine learning tool to find patterns in LIGO data.

The tool can identify different environmental states of interest, such as earthquakes, microseisms, and anthropogenic noise, across a number of carefully selected and curated sensing channels.

The technology is also potentially applicable to large scale particle accelerator experiments and large complex industrial systems.

The hope is that the tool can shed light on physical noise coupling pathways that allow for actionable experimental changes to the LIGO detectors.

The long-term goal is to use the tool to detect new associations and new forms of environmental states associated with unknown noise problems in the interferometers.

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