Dipole-coupled heavy-neutral-lepton explanations of the MiniBooNE excess including constraints from MINERvA data
Bibliographic record
Abstract
We revisit models of heavy neutral leptons (neutrissimos) with transition magnetic moments as explanations of the $4.8\ensuremath{\sigma}$ excess of electronlike events at MiniBooNE. We first reexamine the preferred regions in the model parameter space to explain MiniBooNE, considering also potential contributions from oscillations due to an eV-scale sterile neutrino. We then derive constraints on the model using neutrino-electron elastic scattering data from MINERvA. To carry out these analyses, we have developed a detailed Monte Carlo simulation of neutrissimo interactions within the MiniBooNE and MINERvA detectors using the LeptonInjector framework. This simulation allows for a significantly more robust evaluation of the neutrissimo model compared to previous studies in the literature---a necessary step in order to begin making definitive statements about beyond the Standard Model explanations of the MiniBooNE excess. We find that MINERvA rules out a large region of parameter space, but allowed solutions exist at the $2\ensuremath{\sigma}$ confidence level. A dedicated MINERvA analysis would likely be able to probe the entire region of preference of MiniBooNE in this 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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| 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 | 0.002 | 0.002 |
| 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".