Gamma-Ray Urgent Archiver for Novel Opportunities (GUANO): Swift/BAT Event Data Dumps on Demand to Enable Sensitive Subthreshold GRB Searches
Bibliographic record
Abstract
Abstract We introduce a new capability of the Neil Gehrels Swift Observatory to provide event-level data from the Burst Alert Telescope (BAT) on demand in response to transients detected by other instruments. We show that the availability of these data can effectively increase the rate of detections and arcminute localizations of gamma-ray bursts (GRB) like GRB 170817 by >400%. We describe an autonomous spacecraft-commanding pipeline purpose built to enable this science; to our knowledge, this is 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 2020 January, and has resulted in the recovery of BAT event data for >800 externally triggered events to date (gravitational waves, GWs; neutrinos; GRBs triggered by other facilities; fast radio bursts; and very high-energy detections), 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 upper limits on prompt 1 s duration short GRB-like emission within ±15 s around the unmodeled GW burst candidate S200114f, and (2) provide an arcminute localization for short GRB 200325A and other bursts. We also show that using data from GUANO to localize GRBs discovered by other instruments, 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 subthreshold GRBs, the new capabilities designed for this project will serve as the foundation for further automation and rapid target of opportunity capabilities for the Swift mission, and have implications for the design of future rapid-response space telescopes.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.003 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".