An Archival Search for Neutron-star Mergers in Gravitational Waves and Very-high-energy Gamma Rays
Bibliographic record
Abstract
Abstract The recent discovery of electromagnetic signals in coincidence with neutron-star mergers has solidified the importance of multimessenger campaigns in studying the most energetic astrophysical events. Pioneering multimessenger observatories, such as LIGO/Virgo and IceCube, record many candidate signals below the detection significance threshold. These sub-threshold event candidates are promising targets for multimessenger studies, as the information provided by them may, when combined with contemporaneous gamma-ray observations, lead to significant detections. Here we describe a new method that uses such candidates to search for transient events using archival very-high-energy gamma-ray data from imaging atmospheric Cherenkov telescopes (IACTs). We demonstrate the application of this method to sub-threshold binary neutron star (BNS) merger candidates identified in Advanced LIGO’s first observing run. We identify eight hours of archival VERITAS observations coincident with seven BNS merger candidates and search them for TeV emission. No gamma-ray emission is detected; we calculate upper limits on the integral flux and compare them to a short gamma-ray burst model. We anticipate this search method to serve as a starting point for IACT searches with future LIGO/Virgo data releases as well as in other sub-threshold studies for multimessenger transients, such as IceCube neutrinos. Furthermore, it can be deployed immediately with other current-generation IACTs, and has the potential for real-time use that places a minimal burden on experimental operations. Lastly, this method may serve as a pilot for studies with the Cherenkov Telescope Array, which has the potential to observe even larger fields of view in its divergent pointing mode.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".