Reconciling collider signals, dark matter, and the muon anomalous magnetic moment in the supersymmetric U(1)R × U(1)B−L model
Bibliographic record
Abstract
Abstract We study the low-scale predictions of the supersymmetric model extended by U(1)R × U(1)B−L symmetry, obtained by breaking SO(10) symmetry at GUT scale via a left-right supersymmetric model. Two new singlet Higgs fields $$ \left({\upchi}_R,{\overline{\upchi}}_R\right) $$ χ R χ ¯ R are responsible for the U(1)R × U(1)B−L symmetry breaking to the standard model gauge group. We explore the phenomenology of this model by assuming universal and non-universal boundary conditions at the GUT scale and their effects in obtaining consistency among low-energy observables, dark matter experiments, muon magnetic moment measurements, and Z′ phenomenology. We examine different scenarios with both the lightest neutralino and sneutrino mass eigenstates as dark matter candidates. We explore the collider signals of various scenarios by including relevant benchmarks and exploring their significance versus standard model background. To complement our analysis, we perform recasting of several LHC analyses to verify the credibility of the benchmarks. We find that relaxing the universality conditions at MGUT can significantly improve the agreement of the model against the experimental bounds. While the muon anomalous magnetic moment is found to be the most challenging observable to fit within the model, we identify, allowing for non-universality at the GUT scale, points in the parameter space consistent within 2σ from the average measured value.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.001 |
| 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 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".