Unfolding the Muon Neutrino Spectrum with Eleven Years of IceCube Data
Bibliographic record
Abstract
The IceCube Neutrino Observatory, a cubic-kilometre detector embedded in the glacial ice of the South Pole, is designed to detect neutrinos across a broad energy range, from a few GeV to several PeV. This enables precise measurements of the neutrino energy spectrum, comprising the diffuse astrophysical flux, the conventional atmospheric flux from pion and kaon decays, and the not yet detected prompt neutrino flux from charmed hadron decays. Investigating the prompt component, expected to dominate in the crossover region between the other two, is a critical focus for understanding neutrino interactions, atmospheric processes, and cosmic ray composition. This analysis determines the muon neutrino energy spectrum in the sensitive energy range between 500 GeV and 4 PeV with eleven years of IceCube data. We used an unfolding technique, which allows for model-independent determination and re-bins the observable space to ensure sufficient statistics at the highest energies. In addition to improving the precision of intermediate-energy spectral measurements, it provides the first reconstruction of the muon neutrino flux across five zenith angle bins from 86° to 180°, increasing IceCube’s energy range and enabling comparisons with theoretical models and prior measurements.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".