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Utilizing aLIGO Glitch Classifications to Validate Gravitational-Wave Candidates
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Advanced LIGO data contains numerous noise transients, or "glitches", that have been shown to reduce the sensitivity of matched filter searches for gravitational waves from compact binaries by increasing the rate at which random coincidences occur. The presence of these transients has precipitated extensive work to establish that observed gravitational wave events are astrophysical in nature. We discuss the response of the PyCBC search for gravitational waves from stellar mass binaries to various common glitches that were observed during Advanced LIGO's first and second observing runs. We show how these transients can mimic waveforms from compact binary coalescences and quantify the likelihood that a given class of glitches will create a trigger in the search pipeline. We explore the specific waveform parameters that are most similar to different glitch classes and demonstrate how knowledge of these similarities can be used when evaluating the significance of gravitational-wave candidates.
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PINCH: Pipeline-Informed Noise Characterization in LIGO's Third Observing Run
PINCH uses support vector machines trained on clean GstLAL triggers to identify glitch-induced triggers, revealing class-specific patterns in how transient noise contaminates LIGO's third observing run.
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