Constraints on Minute-Scale Transient Astrophysical Neutrino Sources
Bibliographic record
Abstract
High-energy neutrino emission has been predicted for several short-lived astrophysical transients including gamma-ray bursts (GRBs), core-collapse supernovae with choked jets, and neutron star mergers. IceCube's optical and x-ray follow-up program searches for such transient sources by looking for two or more muon neutrino candidates in directional coincidence and arriving within 100 s. The measured rate of neutrino alerts is consistent with the expected rate of chance coincidences of atmospheric background events and no likely electromagnetic counterparts have been identified in Swift follow-up observations. Here, we calculate generic bounds on the neutrino flux of short-lived transient sources. Assuming an ${E}^{\ensuremath{-}2.5}$ neutrino spectrum, we find that the neutrino flux of rare sources, like long gamma-ray bursts, is constrained to $<5%$ of the detected astrophysical flux and the energy released in neutrinos (100 GeV to 10 PeV) by a median bright GRB-like source is $<1{0}^{52.5}\text{ }\text{ }\mathrm{erg}$. For a harder ${E}^{\ensuremath{-}2.13}$ neutrino spectrum up to 30% of the flux could be produced by GRBs and the allowed median source energy is $<{10}^{52}\text{ }\text{ }\mathrm{erg}$. A hypothetical population of transient sources has to be more common than ${10}^{\ensuremath{-}5}\text{ }\text{ }{\mathrm{Mpc}}^{\ensuremath{-}3}\text{ }\text{ }{\mathrm{yr}}^{\ensuremath{-}1}$ ($5\ifmmode\times\else\texttimes\fi{}{10}^{\ensuremath{-}8}\text{ }\text{ }{\mathrm{Mpc}}^{\ensuremath{-}3}\text{ }\text{ }{\mathrm{yr}}^{\ensuremath{-}1}$ for the ${E}^{\ensuremath{-}2.13}$ spectrum) to account for the complete astrophysical neutrino flux.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 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) | 0.003 | 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".