Bibliographic record
Abstract
Given the shortcomings of the SM described in Sect. 1.6.1 , there have been many efforts to develop a more complete theory which gives rise to the same predictions as the SM at low energies, but incorporates Beyond Standard Model (BSM) physics at higher scales. These range from “Theories of Everything” such as string theory, to “Grand Unified Theories” which unify the three gauge symmetries of the SM in one single gauge symmetry such as $$\text {SU}(5)$$ [1], to simple extensions to the SM gauge group via the inclusion of additional gauge symmetries. In this chapter, we will discuss some of these theories with an emphasis on phenomenology and prospect for discovery at hadron colliders. Section 2.1 introduces the concept of supersymmetry, which posits an additional symmetry of spacetime that can be used to simultaneously solve both the hierarchy problem as well as provide potential dark matter candidates. Section 2.2 then considers alternative scenarios that share similarities with supersymmetric models while evading certain experimental constraints. The phenomenology of these models is described in Sect. 2.3, which leads us to a general overview of long-lived particles in Sect. 2.4. Section 2.5 introduces a simplified class of models that can be used to search for new physics in a model independent way, and a summary of existing constraints on these simplified models is summarized in Sect. 2.6 which provides further motivation for the search presented in this thesis.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.012 | 0.002 |
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".