Constraints on heavy neutral leptons interacting with a singlet scalar
Bibliographic record
Abstract
Heavy neutral leptons (HNLs) are an attractive minimal extension of the Standard Model, as is a singlet scalar $s$ mixing with the Higgs boson. If both are present, it is natural for HNLs to interact with $s$. For a light singlet, the decay $N\ensuremath{\rightarrow}s\ensuremath{\nu}$ can dominate over weak HNL decays. We reinterpret existing constraints on HNL mixing from the DELPHI, CHARM, and Belle experiments for 0.5--100 GeV mass HNLs, taking into account the new decay channel. Although the constraints are typically weakened, in some cases they can become stronger, due to observable $s\ensuremath{\rightarrow}{\ensuremath{\ell}}^{+}{\ensuremath{\ell}}^{\ensuremath{-}}$ decays in the detectors. The method presented here could be used to recast constraints from other (older) experiments without resorting to computationally expensive Monte Carlo simulations. In addition, we update and correct some errors in the analysis of the original constraints, in the absence of the singlet.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".