Cosmogenic neutron production at Daya Bay
Bibliographic record
Abstract
Neutrons produced by cosmic ray muons are an important background for underground experiments studying neutrino oscillations, neutrinoless double beta decay, dark matter, and other rare-event signals. A measurement of the neutron yield in the three different experimental halls of the Daya Bay Reactor Neutrino Experiment at varying depth is reported. The neutron yield in Daya Bay's liquid scintillator is measured to be ${Y}_{n}=(10.26\ifmmode\pm\else\textpm\fi{}0.86)\ifmmode\times\else\texttimes\fi{}{10}^{\ensuremath{-}5}$, $(10.22\ifmmode\pm\else\textpm\fi{}0.87)\ifmmode\times\else\texttimes\fi{}{10}^{\ensuremath{-}5}$, and $(17.03\ifmmode\pm\else\textpm\fi{}1.22)\ifmmode\times\else\texttimes\fi{}{10}^{\ensuremath{-}5}\text{ }\text{ }{\ensuremath{\mu}}^{\ensuremath{-}1}\text{ }{\mathrm{g}}^{\ensuremath{-}1}\text{ }{\mathrm{cm}}^{2}$ at depths of 250, 265, and 860 meters-water-equivalent. These results are compared to other measurements and the simulated neutron yield in Fluka and Geant4. A global fit including the Daya Bay measurements yields a power law coefficient of $0.77\ifmmode\pm\else\textpm\fi{}0.03$ for the dependence of the neutron yield on muon energy.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".