From Dark Matter to Leptoquarks: Phenomenology of Physics Beyond the Standard Model
Bibliographic record
Abstract
The Standard Model of particle physics began as a simple ``Model of Leptons" in 1967, but after more than five decades of theoretical development and experimental scrutiny it has grown to become the most successful scientific theory of all time.We begin with a short summary of the features of the Standard Model and a discussion of its explanatory power, taking the modern perspective. We then highlight three particular shortcomings of the SM in its current formulation: the electroweak hierarchy problem, the nature of dark matter, and the growing list of anomalies in the flavour sector, neutrino sector, and elsewhere. These shortcomings form the foundation of the work presented in this thesis. We first show that the electroweak hierarchy problem and the nature of dark matter can be understood through the combination of a general Twin Higgs framework and an additional singlet scalar acting as dark matter. Then we turn our attention to minimal Twin Higgs models and reexamine the twin tau lepton as a dark matter candidate, in light of recent theoretical developments into the UV structure of these models. We find that the scenario is possible and may be discoverable in future direct detection experiments. Lastly, we search for a novel explanation to the MiniBooNE anomaly using scalar leptoquarks to induce lepton flavour violating pion decays, and consider $\mu-e$ conversion experiments as a future probe of important leptoquark couplings.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".