The future of high-energy astrophysical neutrino flavor measurements
Bibliographic record
Abstract
Abstract We critically examine the ability of future neutrino telescopes, including Baikal-GVD, KM3NeT, P-ONE, TAMBO, and IceCube-Gen2, to determine the flavor composition of high-energy astrophysical neutrinos in light of data from next-generation of neutrino oscillation experiments including JUNO, DUNE, and Hyper-Kamiokande. By 2040, the region of allowed flavor composition at Earth will shrink ten-fold, and the flavor composition at the astrophysical sources of the neutrinos will be inferred to within 6%, enough to pinpoint the dominant neutrino production mechanism and to identify possible sub-dominant mechanisms. These conclusions hold even in the nonstandard scenario where neutrino mixing is non-unitary, a scenario that will be probed in next-generation experiments such as the IceCube-Upgrade. As an illustration, we show that future experiments are sensitive to decay rates of the heavier neutrinos to below 1.8 × 10 -5 (m/eV) s -1 at 95% credibility by 2040.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.017 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.006 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".