Panning for gold with the Neil Gehrels Swift Observatory: an optimal strategy for finding the counterparts to gravitational wave events
R. A. J. Eyles-Ferris, P. A. Evans, A. A. Breeveld, S. B. Cenko, S., Dichiara, J. A. Kennea, N. J. Klingler, N. P. M. Kuin, F. E. Marshall, S. R., Oates, M. J. Page, S. Ronchini, M. H. Siegel, A. Tohuvavohu, S. Campana, V., D'Elia, J. P. Osborne, K. L. Page, M. De Pasquale

TL;DR
This paper evaluates the effectiveness of the Neil Gehrels Swift Observatory in detecting electromagnetic counterparts to gravitational wave events, proposing optimal strategies for follow-up observations during the O4 run.
Contribution
It introduces an optimized follow-up strategy for Swift to efficiently identify counterparts to gravitational wave events, especially binary neutron star mergers.
Findings
UVOT with 120 s exposure in the u filter is optimal for most follow-ups.
Approximately 6% of binary neutron star triggers are detectable by Swift in O4.
Focusing on sources within 300 Mpc improves detection probabilities to 5-30%.
Abstract
The LIGO, Virgo and KAGRA gravitational wave observatories are currently undertaking their O4 observing run offering the opportunity to discover new electromagnetic counterparts to gravitational wave events. We examine the capability of the Neil Gehrels Swift Observatory (Swift) to respond to these triggers, primarily binary neutron star mergers, with both the UV/Optical Telescope (UVOT) and the X-ray Telescope (XRT). We simulate Swift's response to a trigger under different strategies using model skymaps, convolving these with the 2MPZ catalogue to produce an ordered list of observing fields, deriving the time taken for Swift to reach the correct field and simulating the instrumental responses to modelled kilonovae and short gamma-ray burst afterglows. We find that UVOT using the filter with an exposure time of order 120 s is optimal for most follow-up observations and that we are…
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Taxonomy
TopicsGeophysics and Gravity Measurements · Pulsars and Gravitational Waves Research · Statistical and numerical algorithms
