Improved Search for a Light Sterile Neutrino with the Full Configuration of the Daya Bay Experiment
Bibliographic record
Abstract
This Letter reports an improved search for light sterile neutrino mixing in the electron antineutrino disappearance channel with the full configuration of the Daya Bay Reactor Neutrino Experiment. With an additional 404 days of data collected in eight antineutrino detectors, this search benefits from 3.6 times the statistics available to the previous publication, as well as from improvements in energy calibration and background reduction. A relative comparison of the rate and energy spectrum of reactor antineutrinos in the three experimental halls yields no evidence of sterile neutrino mixing in the $2\ifmmode\times\else\texttimes\fi{}{10}^{\ensuremath{-}4}\ensuremath{\lesssim}|\mathrm{\ensuremath{\Delta}}{m}_{41}^{2}|\ensuremath{\lesssim}0.3\text{ }\text{ }{\mathrm{eV}}^{2}$ mass range. The resulting limits on ${\mathrm{sin}}^{2}2{\ensuremath{\theta}}_{14}$ are improved by approx imately a factor of 2 over previous results and constitute the most stringent constraints to date in the $|\mathrm{\ensuremath{\Delta}}{m}_{41}^{2}|\ensuremath{\lesssim}0.2\text{ }\text{ }{\mathrm{eV}}^{2}$ region.
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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 | 0.005 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".