Constraining sterile neutrinos using reactor neutrino experiments
Bibliographic record
Abstract
Models of neutrino mixing involving one or more sterile neutrinos have resurrected their importance in the light of recent cosmological data. In this case, reactor antineutrino experiments offer an ideal place to look for signatures of sterile neutrinos due to their impact on neutrino flavor transitions. In this work, we show that the high-precision data of the Daya Bay experiment constrain the 3+1 neutrino scenario imposing upper bounds on the relevant active-sterile mixing angle sin2 2θ 14 ≲ 0.06 at 3σ confidence level for the mass-squared difference Δm 41 2 in the range (10−3, 10−1) eV2. The latter bound can be improved by six years of running of the JUNO experiment, sin2 2θ 14 ≲ 0.016, although in the smaller mass range Δm 41 2 ∈ (10−4, 10−3) eV2. We have also investigated the impact of sterile neutrinos on precision measurements of the standard neutrino oscillation parameters θ 13 and Δm 31 2 (at Daya Bay and JUNO), θ 12 and Δm 21 2 (at JUNO), and most importantly, the neutrino mass hierarchy (at JUNO). We find that, except for the obvious situation where Δm 41 2 ≲ Δm 31 2 , sterile states do not affect these measurements substantially.
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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 | 0.007 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".