Neutron-antineutron oscillation search using a 0.37 megaton-years exposure of Super-Kamiokande
Bibliographic record
Abstract
As a baryon number violating process with $\mathrm{\ensuremath{\Delta}}B=2$, neutron-antineutron oscillation ($n\ensuremath{\rightarrow}\overline{n}$) provides a unique test of baryon number conservation. We have performed a search for $n\ensuremath{\rightarrow}\overline{n}$ oscillation with bound neutrons in Super-Kamiokande, with the full dataset from its first four run periods, representing an exposure of $0.37\text{ }\text{ }\mathrm{Mton}\text{\ensuremath{-}}\mathrm{years}$. The search used a multivariate analysis trained on simulated $n\ensuremath{\rightarrow}\overline{n}$ events and atmospheric neutrino backgrounds and resulted in 11 candidate events with an expected background of 9.3 events. In the absence of statistically significant excess, we derived a lower limit on $\overline{n}$ appearance lifetime in $^{16}\mathrm{O}$ nuclei of $3.6\ifmmode\times\else\texttimes\fi{}{10}^{32}\text{ }\text{ }\mathrm{years}$ and on the neutron-antineutron oscillation time of ${\ensuremath{\tau}}_{n\ensuremath{\rightarrow}\overline{n}}>4.7\ifmmode\times\else\texttimes\fi{}{10}^{8}\text{ }\text{ }\mathrm{s}$ at 90% C.L.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".