Electroweak precision measurements in supersymmetric models with a U(1) R lepton number
Bibliographic record
Abstract
As experimental constraints on the parameter space of the MSSM and close variations thereof become stronger, the motivation to explore supersymmetric models that challenge some of the standard assumptions of the MSSM also become stronger. For example, models where the gauginos are Dirac instead of Majorana have recently received more attention. Beside allowing for a supersoft SUSY breaking mechanism where the gauginos only provide finite threshold corrections to scalar masses, the cross section for the production of a squark pairs is reduced. In addition, Dirac gauginos can be used to build models that possess a U(1)R symmetry. This symmetry can then be identified with a lepton number, leading to models that are quite different from conventional scenarios. The sneutrinos in these models can acquire a vev and give mass to the leptons and the down-type squark. The phenomenology is novel, combining signatures that are typical of R-parity violating scenarios with signatures arising from leptoquarks. Correspondingly the constraints from electroweak precision data are also different. In these models, one of the leptons mixes with gauginos and superpotential Yukawa couplings can contribute to EWPM at tree level. In addition, lepton universality is broken. In this paper we adapt the operators analysis of Han and Skiba [1] to include the relevant violation of lepton universality, and do a global fit of the model to electroweak precision data, including all relevant tree-level and loop-level effects. We obtain bounds on the vev of the sneutrino and on the superpotential couplings of the 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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 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) | 0.001 | 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".