Improved measurement of the reactor antineutrino flux at Daya Bay
Bibliographic record
Abstract
This work reports a precise measurement of the reactor antineutrino flux using 2.2 million inverse beta decay (IBD) events collected with the Daya Bay near detectors in 1230 days. The dominant uncertainty on the neutron detection efficiency is reduced by 56% with respect to the previous measurement through a comprehensive neutron calibration and detailed data and simulation analysis. The new average IBD yield is determined to be $(5.91\ifmmode\pm\else\textpm\fi{}0.09)\ifmmode\times\else\texttimes\fi{}{10}^{\ensuremath{-}43}\text{ }\text{ }{\mathrm{cm}}^{2}/\text{fission}$ with total uncertainty improved by 29%. The corresponding mean fission fractions from the four main fission isotopes $^{235}\mathrm{U}$, $^{238}\mathrm{U}$, $^{239}\mathrm{Pu}$, and $^{241}\mathrm{Pu}$ are 0.564, 0.076, 0.304, and 0.056, respectively. The ratio of measured to predicted antineutrino yield is found to be $0.952\ifmmode\pm\else\textpm\fi{}0.014\ifmmode\pm\else\textpm\fi{}0.023$ ($1.001\ifmmode\pm\else\textpm\fi{}0.015\ifmmode\pm\else\textpm\fi{}0.027$) for the Huber-Mueller (ILL-Vogel) model, where the first and second uncertainty are experimental and theoretical model uncertainty, respectively. This measurement confirms the discrepancy between the world average of reactor antineutrino flux and the Huber-Mueller model.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".