Optimized Search for a Binary Black Hole Merger Population in LIGO-Virgo O3 Data

Abstract

Maximizing the number of detections in matched filter searches for compact binary coalescence (CBC) gravitational wave (GW) signals requires a model of the source population distribution. In previous searches using the PyCBC framework, sensitivity to the population of binary black hole (BBH) mergers was improved by restricting the range of filter template mass ratios and use of a simple one-dimensional population model. However, this approach does not make use of our full knowledge of the population and cannot be extended to a full parameter space search. Here, we introduce a new ranking method, based on kernel density estimation (KDE) with adaptive bandwidth, to accurately model the probability distributions of binary source parameters over a template bank, both for signals and for noise events. We demonstrate this ranking method by conducting a search over LIGO-Virgo O3 data for BBH with unrestricted mass ratio, using a signal model derived from previous significant detected events. We achieve over 10% increase in sensitive volume for a simple power-law simulated signal population, compared to the previous BBH search. Correspondingly, with the new ranking, 8 additional candidate events above an inverse false alarm rate (IFAR) threshold 0.5 yr are identified.

19 pages, 7 figures, 2 tables

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• Advanced Virgo: a 2nd generation interferometric gravitational wave detector
• Advanced LIGO
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• Multipolar Effective-One-Body Waveforms for Precessing Binary Black Holes: Construction and Validation
• Implementing a search for aligned-spin neutron star -- black hole systems with advanced ground based gravitational wave detectors
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• Search for Precessing Binary Black Holes in Advanced LIGO's Third Observing Run using Harmonic Decomposition
• Inflationary phase transitions in the early Universe: A Bayesian study with space-based gravitational-wave detectors