Optimising the choice of analysis method for all-sky searches for continuous gravitational waves with Einstein@Home

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.