Probing neutrino production in high-energy astrophysical neutrino sources with the Glashow resonance
Bibliographic record
Abstract
The flavor composition of high-energy neutrinos carries important information about their birth. However, the two most common production scenarios, $pp$ (hadronuclear) and $p\ensuremath{\gamma}$ (photohadronic) collisions, lead to the same flavor ratios when neutrinos and antineutrinos cannot be distinguished. The Glashow resonant interaction ${\overline{\ensuremath{\nu}}}_{e}+{e}^{\ensuremath{-}}\ensuremath{\rightarrow}{W}^{\ensuremath{-}}$ becomes a window to differentiate the antineutrino contribution from the total diffuse neutrino flux, thus lifting this degeneracy. We examine the power of Glashow resonant events in measuring the fraction of the ${\overline{\ensuremath{\nu}}}_{e}$ flux with current IceCube data, and produce projected sensitivities based on the combined exposure of planned Cherenkov neutrino telescopes around the globe. We find that $pp$ and $p\ensuremath{\gamma}$ can be distinguished at a $2\ensuremath{\sigma}$ significance level in the next decades, in both an event-wise analysis and a more conservative statistical analysis, even with pessimistic assumptions on the spectral index of the astrophysical flux. Finally, we consider the sensitivity of future experiments to mixed production mechanisms.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.002 | 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".