Search for heavy neutrinos with the T2K near detector ND280
Bibliographic record
Abstract
This paper reports on the search for heavy neutrinos with masses in the range $140<{M}_{N}<493\text{ }\text{ }\mathrm{MeV}/{\mathrm{c}}^{2}$ using the off-axis near detector ND280 of the T2K experiment. These particles can be produced from kaon decays in the standard neutrino beam and then subsequently decay in ND280. The decay modes under consideration are $N\ensuremath{\rightarrow}{\ensuremath{\ell}}_{\ensuremath{\alpha}}^{\ifmmode\pm\else\textpm\fi{}}{\ensuremath{\pi}}^{\ensuremath{\mp}}$ and $N\ensuremath{\rightarrow}{\ensuremath{\ell}}_{\ensuremath{\alpha}}^{+}{\ensuremath{\ell}}_{\ensuremath{\beta}}^{\ensuremath{-}}\stackrel{(\ensuremath{-})}{\ensuremath{\nu}}(\ensuremath{\alpha},\ensuremath{\beta}=e,\ensuremath{\mu})$. A search for such events has been made using the Time Projection Chambers of ND280, where the background has been reduced to less than two events in the current dataset in all channels. No excess has been observed in the signal region. A combined Bayesian statistical approach has been applied to extract upper limits on the mixing elements of heavy neutrinos to electron-, muon- and tau- flavored currents (${U}_{e}^{2}$, ${U}_{\ensuremath{\mu}}^{2}$, ${U}_{\ensuremath{\tau}}^{2}$) as a function of the heavy neutrino mass, e.g., ${U}_{e}^{2}<{10}^{\ensuremath{-}9}$ at 90% C.L. for a mass of $390\text{ }\text{ }\mathrm{MeV}/{\mathrm{c}}^{2}$. These constraints are competitive with previous experiments.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".