Effects of tau-neutrino detection on non-standard interactions at DUNE with a short discussion on the nature of neutrino mixing
Bibliographic record
Abstract
A bstract In this paper, we investigate the effects of ν τ and $$ {\overline{\nu}}_{\tau } $$ ν ¯ τ detection at the DUNE far detector on the experiment’s sensitivity to Non-Standard Interactions (NSI) in neutrino propagation. We show that the strongest observable NSI effect in the ν τ and $$ {\overline{\nu}}_{\tau } $$ ν ¯ τ appearance probabilities arises from ϵ μτ . We have studied the hierarchy sensitivity, CP violation sensitivity and octant sensitivity of DUNE from ν τ and $$ {\overline{\nu}}_{\tau } $$ ν ¯ τ appearance channels in presence of NSI. We have also studied the detection sensitivity of NSI phases and the future constaints on NSI parameters from the tau neutrino appearance channels in DUNE. Additionally, we examine the role of ν τ detection in constraining the unitary nature of the PMNS matrix. These studies emphasize the importance of incorporating ν τ detection in long-baseline neutrino experiments such as DUNE.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".