Results from IceCube Searches for High-Energy Neutrinos Coincident with Gravitational-Wave Alerts in LVK O4
Bibliographic record
Abstract
Mergers of compact objects, binary black holes and mergers including at least one neutron star, are a predicted source of high-energy neutrinos. These astrophysical events are now routinely detected through observation of their gravitational wave signature and, at least in one instance, their electromagnetic counterparts were also detected. Particles accelerated during the coalescence of compact objects may also interact to produce high-energy neutrinos, which have yet to be detected, but observations are ongoing. The LIGO-Virgo-KAGRA Collaboration publicly releases information on candidate gravitational wave events from compact binary coalescences in low latency during the current observing run (O4). To aid the electromagnetic follow-up, using data from the IceCube Neutrino Observatory, we search, in real time, for neutrinos spatially and temporally coincident with these gravitational wave candidate events using a time window of 1000 seconds centered on the merger time. We use two methods, both of which have been previously used to search for neutrino emission from gravitational-wave transients: an unbinned maximum likelihood analysis applied to significant alerts and a Bayesian analysis with astrophysical priors, applied to both significant and low-significance alerts. In addition, we search for long-duration neutrino emission up to 14 days after the merging of binaries containing a neutron star. We report analysis results determined in real time for these searches, and set upper limits on both flux and isotropic-equivalent energy emitted in neutrinos.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 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) | 0.002 | 0.001 |
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".