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Abstract

Rapidly rotating neutron stars are promising sources of continuous
gravitational waves for the LIGO and Virgo observatories. The majority of
neutron stars in our galaxy have not been identified with electromagnetic
observations. Blind all-sky searches offer the potential to detect
gravitational waves from these unidentified sources. The parameter space of
these searches presents a significant computational challenge. Various methods
have been designed to perform these searches with available computing
resources. Recently, a method called Weave has been proposed to achieve
template placement with a minimal number of templates. We employ a mock data
challenge to assess the ability of this method to recover signals, and compare
its sensitivity with that of the global correlation transform method (GCT),
which has been used for searches with the Einstein@Home volunteer computing
project for a number of years. We find that the Weave method is 14% more
sensitive for an all-sky search on Einstein@Home, with a sensitivity depth of
$57.9\pm0.6$ 1/$\sqrt{Hz}$ at 90% detection efficiency, compared to
$50.8^{+0.7}_{-1.1}$ 1/$\sqrt{Hz}$ for GCT. This corresponds to a 50% increase
in the volume of sky where we are sensitive with the Weave search. We also find
that the Weave search recovers candidates closer to the true signal position.
In the search studied here the improvement in candidate localisation would lead
to a factor of 70 reduction in the computing cost required to follow up the
same number of candidates. We assess the feasability of deploying the search on
Einstein@Home, and find that Weave requires more memory than is typically
available on a volunteer computer. We conclude that, while GCT remains the best
choice for deployment on Einstein@Home due to its lower memory requirements,
Weave presents significant advantages for the subsequent hierarchical follow-up
of interesting candidates.