Making the sneutrino a Higgs particle with a<mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline"><mml:mi>U</mml:mi><mml:mo stretchy="false">(</mml:mo><mml:mn>1</mml:mn><mml:msub><mml:mo stretchy="false">)</mml:mo><mml:mi>R</mml:mi></mml:msub></mml:math>lepton number
Bibliographic record
Abstract
We present a supersymmetric extension of the Sstandard model that possesses a continuous $U(1{)}_{R}$ symmetry, which is identified with one of three lepton numbers, and where a sneutrino vev gives mass to the down-type quark and leptons. This idea allows for a smaller particle content than the minimal $R$-symmetric supersymmetry extension of the standard model. We explore bounds on this model coming from electroweak precision measurements, neutrino masses, and gravitino decay. Bounds from electroweak precision measurements lead to a two-sided bound on $\mathrm{tan}\ensuremath{\beta}$, while gravitino decay forces a low reheating temperature. Finally, the generation of neutrino masses from $R$-symmetry violation put an upper bound on the SUSY-breaking scale. Despite all of this, we find that the allowed parameter space is still large and would lead to a distinctive phenomenology at the LHC.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.007 | 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".