Identifying energy-dependent flavor transitions in high-energy astrophysical neutrino measurements
Bibliographic record
Abstract
The flavor composition of TeV---PeV astrophysical neutrinos, i.e., the proportion of neutrinos of different flavors in their flux, is a versatile probe of high-energy astrophysics and fundamental physics. Because flavor identification is challenging and the number of detected high-energy astrophysical neutrinos is limited, so far measurements of the flavor composition have represented an average over the range of observed neutrino energies. Yet, this washes out the potential existence of changes in the flavor composition with energy and weakens our sensitivity to the many models that posit them. For the first time, we measure the energy dependence of the flavor composition, looking for a transition from low to high energies. Our present-day measurements, based on the 7.5-year public sample of IceCube high-energy starting events (HESE), find no evidence of a flavor transition. The observation of HESE and through-going muons jointly by next-generation neutrino telescopes Baikal-GVD, IceCube-Gen2, KM3NeT, P-ONE, TAMBO, and TRIDENT may identify a flavor transition around 200 TeV by 2030. By 2040, we could infer the flavor composition with which neutrinos are produced with enough precision to establish the transition from neutrino production via the full pion decay chain at low energies to muon-damped pion decay at high energies.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| 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) | 0.001 | 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".