A two-step strategy to identify episodic sources of gravitational waves and high-energy neutrinos in starburst galaxies
Bibliographic record
Abstract
Supermassive black hole (BH) mergers with spin-flips accelerate energetic particles through their relativistic precessing jets, producing high-energy neutrinos and finally gravitational waves (GWs). In star formation, massive stars form in pairs, triplets, and quadruplets, allowing second-generation mergers of the remnants with discrepant spin directions. The GW data support such a scenario. Earlier, we suggested that stellar mass BH mergers (visible in M82) with an associated spin-flip analogously allow the acceleration of energetic particles, with ensuing high-energy neutrinos and high-energy photons, and finally produce GWs. At cosmic distances, only the GWs and the neutrinos remain detectable. In this study, we generalize the argument to starburst and normal galaxies throughout their cosmic evolution and show that these galaxies may dominate over active galactic nuclei (AGN) in the flux of ultra-high-energy particles observed at Earth. All these sources contribute to the cosmic neutrino background, as well as the GW background (they detected lower frequencies). We outline a search strategy to find such episodic sources, which requires including both luminosity and flux density.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".