Harvesting BAT-GUANO with NITRATES (Non-Imaging Transient Reconstruction and Temporal Search): Detecting and Localizing the Faintest Gamma-Ray Bursts with a Likelihood Framework
Bibliographic record
Abstract
Abstract The detection of the gravitational wave (GW) counterpart GRB 170817A, underluminous compared to the cosmological gamma-ray burst (GRB) population by a factor of 10,000, motivates significant effort in detecting and localizing a dim, nearby, and slightly off-axis population of short GRBs. Swift’s Burst Alert Telescope (BAT) is one of the most sensitive GRB detectors in operation, and the only one that regularly localizes GRBs to arcminute precision, critical to rapid follow-up studies. However, the utility of BAT in targeted subthreshold searches had been historically curtailed by the unavailability of the necessary raw data for analysis. The new availability of time-tagged event data from the GUANO system motivates a renewed focus on developing sensitive targeted search analysis techniques to maximally exploit these data. While computationally cheap, we show that the typical coded-mask deconvolution imaging is limited in its sensitivity due to several factors. We formalize a maximum likelihood framework for the analysis of BAT data wherein signals are forward modeled through the full instrument response, and—coupled with the development of new response models—demonstrate its superior sensitivity to typical imaging via archival comparisons, injection campaigns, and a large number of low-latency GRB discoveries and confirmed arcminute localizations to date. We also demonstrate independent localization of some out-of-field-of-view GRBs for the first time. NITRATES’s increased sensitivity boosts the discovery rate of GRB 170817A–like events in BAT by a factor of at least 3−4×, along with enabling joint analyses and searches with other GRB, GW, neutrino, and FRB instruments. We provide public access to the response functions and search pipeline code.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".