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Abstract:
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Ground-based gravitational-wave detector data is non-stationary and contains a high rate of transient noise artifacts. This transient noise can mimic or obscure true astrophysical gravitational-wave events, reducing the effective reach of searches for these signals. This talk will summarize the methods employed by the LIGO, Virgo, and KAGRA collaborations to characterize and mitigate the impact of transient noise, including regression, statistical correlation, and machine learning.
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