Effects of Different Data Quality Veto Methods in the PyCBC Search for Compact Binary Coalescences

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Abstract:: The PyCBC search pipeline has been used since the first gravitational wave detection made by Advanced LIGO and continues to be used today in the search for gravitational waves. To identify gravitational waves from compact binary coalescences, PyCBC runs a matched filtering and chi-squared consistency test to determine significant signal-to-noise ratios and compares triggers to previously modeled templates. To confidently detect gravitational waves, we need to mitigate noisy data, which in return improves the sensitivity of searches. Current veto methods use data quality flags to veto and remove triggers in LIGO data that are believed to have terrestrial origin, though these methods risk accidentally removing signals and must be finely tuned to prevent a decrease in the search sensitivity. In this investigation, we test different veto methods based on the current set of data quality flags and detector characterization tools. We analyze how simulated signals are recovered by the PyCBC pipeline and the overall change in the sensitivity of the pipeline. Our results show an improved veto method that increases the significance of signals and the overall number of detectable signals without removing data. The results of this investigation can be implemented in the PyCBC search pipeline in future observation runs held by LIGO as a data quality tool to improve the search for gravitational waves from compact binary coalescences.

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