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Gamma-ray Urgent Archiver for Novel Opportunities (GUANO): Swift/BAT event data dumps on demand to enable sensitive sub-threshold GRB searches

We introduce a new capability of the Neil Gehrels Swift Observatory, to provide event data from the Burst Alert Telescope (BAT) on demand in response to transients detected by other instruments. These event data are not continuously available due to the large telemetry load, but are critical to recovering weak or sub-threshold GRBs that are not triggered onboard, such as the likely counterparts to GW-detected off-axis binary neutron star mergers. We show that the availability of event data can effectively increase the rate of detections, and arcminute localizations, of GRB 170817-like bursts by >400%. We describe a spacecraft commanding pipeline purpose built to enable this science; to our knowledge the first fully autonomous extremely-low-latency commanding of a space telescope for scientific purposes. This pipeline has been successfully run in its complete form since early 2020, and has resulted in the recovery of BAT event data for >700 externally triggered events to date (GWs, neutrinos, GRBs triggered by other facilities, FRBs), now running with a success rate of ~90%. We exemplify the utility of this new capability by using the resultant data to (1) set the most sensitive (8 sigma) upper limits of $8.1\times10^{-8}$ erg cm$^{-2}$ s$^{-1}$ (14-195 keV) on prompt 1s duration short GRB-like emission within $\pm$ 15s of the unmodelled GW burst candidate S200114f, and (2) provide arcminute localizations for short GRB 200325A and other bursts. We show that using data from GUANO to localize GRBs discovered elsewhere, we can increase the net rate of arcminute localized GRBs by 10-20% per year. Along with the scientific yield of more sensitive searches for sub-threshold GRBs, the new capabilities designed for this project will enable further rapid response Target of Opportunity capabilities for Swift, and have implications for the design of future rapid-response space telescopes.

preprint2020arXivOpen access

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