Differentiating<mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline"><mml:mrow><mml:mi>U</mml:mi><mml:mo stretchy="false">(</mml:mo><mml:mn>1</mml:mn><mml:msup><mml:mrow><mml:mo stretchy="false">)</mml:mo></mml:mrow><mml:mrow><mml:mo>′</mml:mo></mml:mrow></mml:msup></mml:mrow></mml:math>supersymmetric models with right sneutrino and neutralino dark matter
Bibliographic record
Abstract
We perform a detailed analysis of dark matter signals of supersymmetric models containing an extra $U(1{)}^{\ensuremath{'}}$ gauge group. We investigate scenarios in which either the right sneutrino or the lightest neutralino are phenomenologically acceptable dark matter candidates and we explore the parameter spaces of different supersymmetric realisations featuring an extra $U(1{)}^{\ensuremath{'}}$. We impose consistency with low energy observables, with known mass limits for the superpartners and ${Z}^{\ensuremath{'}}$ bosons, as well as with Higgs boson signal strengths, and we moreover verify that predictions for the anomalous magnetic moment of the muon agree with the experimental value and require that the dark matter candidate satisfies the observed relic density and direct and indirect dark matter detection constraints. For the case where the sneutrino is the dark matter candidate, we find distinguishing characteristics among different $U(1{)}^{\ensuremath{'}}$ mixing angles. If the neutralino is the lightest supersymmetric particle, its mass is heavier than that of the light sneutrino in scenarios where the latter is a dark matter candidate, the parameter space is less restricted and differentiation between models is more difficult. We finally comment on the possible collider tests of these models.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.593 | 0.380 |
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".