  The most sensitive search pipelines for gravitational waves from compact
binary mergers use matched filters to extract signals from the noisy data
stream coming from gravitational wave detectors. Matched-filter searches
require banks of template waveforms covering the physical parameter space of
the binary system. Unfortunately, template bank construction can be a
time-consuming task. Here we present a new method for efficiently generating
template banks that utilizes automatic differentiation to calculate the
parameter space metric. Principally, we demonstrate that automatic
differentiation enables accurate computation of the metric for waveforms
currently used in search pipelines, whilst being computationally cheap.
Additionally, by combining random template placement and a Monte Carlo method
for evaluating the fraction of the parameter space that is currently covered,
we show that search-ready template banks for frequency-domain waveforms can be
rapidly generated. Finally, we argue that differentiable waveforms offer a
pathway to accelerating stochastic placement algorithms. We implement all our
methods into an easy-to-use Python package based on the jax framework,
diffbank, to allow the community to easily take advantage of differentiable
waveforms for future searches.
