A machine learning classifier trained on the extended noise environment around gravitational wave candidates improves search sensitivity for heavy, unequal-mass black hole mergers by up to roughly 20 percent.
Beyond GWTC-3: Analysing and verifying new gravitational-wave events from community catalogues
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abstract
The public release of data from the LIGO and Virgo detectors has enabled the identification of potential gravitational wave signals by independent teams using alternative methodologies. In addition to the LIGO-Virgo-KAGRA (LVK) collaboration's GWTC-3 catalogue there have been several additional works claiming the detection of signals in the data from the first three observing runs. In this paper we present an analysis of these new signals using the same analysis workflow which was used to generate the GWTC-2.1 and GWTC-3 catalogues published by the LVK, matching the analysis configuration as closely as possible, and we provide our parameter estimation results in a format comparable to those of the GWTC-3 data release. We also include a discussion of the workflow developed for this analysis.
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Improving gravitational wave search sensitivity with TIER: Trigger Inference using Extended strain Representation
A machine learning classifier trained on the extended noise environment around gravitational wave candidates improves search sensitivity for heavy, unequal-mass black hole mergers by up to roughly 20 percent.