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

Understand

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.