  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.
