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Abstract

Detection of many compact binary coalescences (CBCs) is one of the primary
goals of the present and future ground-based gravitational-wave (GW) detectors.
While increasing the detectors' sensitivities will be crucial in achieving
this, efficient data analysis strategies can play a vital role. With given
computational power in hand, efficient data analysis techniques can expand the
size and dimensionality of the parameter space to search for a variety of GW
sources. Matched filtering based analyses that depend on modeled signals to
produce adequate signal-to-noise ratios for signal detection may miss them if
the parameter space is too restrained. Specifically, the CBC search is
currently limited to non-precessing binaries only, where the spins of the
components are either aligned or anti-aligned to the orbital angular momentum.
A hierarchical search for CBCs is thus well motivated. The first stage of this
search is performed by matched filtering coarsely sampled data with a coarse
template bank to look for candidate events. These candidates are then followed
up for a finer search around the vicinity of an event's parameter space.
Performing such a search leads to enormous savings in computational cost. Here
we report the first successful implementation of the hierarchical search as a
PyCBC-based production pipeline to perform a complete analysis of LIGO
observing runs. With this, we analyze Advanced LIGO's first and second
observing run data. We recover all the events detected by the PyCBC (flat)
search in the first GW catalog, GWTC-1, published by the LIGO-Virgo
collaboration, with nearly the same significance using a scaled background. In
the analysis, we get an impressive factor of 20 speed-up in computation
compared to the flat search. With a standard injection study, we show that the
sensitivity of the hierarchical search remains comparable to the flat search
within the error bars.