Atmospheric neutrino oscillation analysis with external constraints in Super-Kamiokande I-IV
Bibliographic record
Abstract
An analysis of atmospheric neutrino data from all four run periods of Super-Kamiokande optimized for sensitivity to the neutrino mass hierarchy is presented. Confidence intervals for $\mathrm{\ensuremath{\Delta}}{m}_{32}^{2}$, ${\mathrm{sin}}^{2}{\ensuremath{\theta}}_{23}$, ${\mathrm{sin}}^{2}{\ensuremath{\theta}}_{13}$ and ${\ensuremath{\delta}}_{CP}$ are presented for normal neutrino mass hierarchy and inverted neutrino mass hierarchy hypotheses, based on atmospheric neutrino data alone. Additional constraints from reactor data on ${\ensuremath{\theta}}_{13}$ and from published binned T2K data on muon neutrino disappearance and electron neutrino appearance are added to the atmospheric neutrino fit to give enhanced constraints on the above parameters. Over the range of parameters allowed at 90% confidence level, the normal mass hierarchy is favored by between 91.9% and 94.5% based on the combined Super-Kamiokande plus T2K result.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".