Global Neutrino Data Analysis and the Quest to Pin Down $\sin\theta_{13}$ in Different Mixing Matrix Parametrizations
Bibliographic record
Abstract
Hints for sizable $\sin^2 \theta_{13}$ have been reported in earlier global neutrino oscillation data analyses as well as will be reported in this work, and quite recently by the Double Chooz experiment. However, as we enter the era of precision neutrino oscillation experiments, terms linear in $\sin\theta_{13}$ will no longer be negligible, and its sign would affect the extraction of other oscillation parameters. The sign of $\sin\theta_{13}$ also plays a crucial role in the determination of the CP-violating phase. In this work we show that by adopting an alternative parametrization for the Pontecorvo-Maki-Nakagawa-Sakata (PMNS) mixing matrix, one already has a chance to infer the sign of each mixing angle in the conventional parametrization using existing global neutrino data. A weak preference for negative $\sin \theta_{13}$ is found. In particular, the solar data suggest that $\sin\theta_{13} > 0$ while all other data the opposite. This leads to the speculation on whether the Mikheyev-Smirnov-Wolfenstein (MSW) effect is responsible. In this work we found that in the new mixing matrix parametrization, the 68% CL constraints on the three mixing angles are comparable to those estimated in the conventional parametrization adopted in the literature. Owing to the strong correlations among the three mixing angles in the new parametrization, the advantages of doing the global neutrino oscillation analysis using data from past, current, and near future neutrino oscillation experiments shall become manifest.
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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 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".