Bibliographic record
Abstract
In this thesis, we first consider the phenomenology of R-parity violating supersymmetric extensions of the Standard Model. Specifically, a large number of Lepton and Baryon Number violating decays of sparticles to particles are implemented in the Pythia event generator. The augmented generator has then been used to study event properties of these models, mainly at the LHC. During these studies, the development of the so-called SuSy Les Houches Accord opened for an easier interfacing between SuSy calculational tools. A first `proof-of-concept' application of the Accord was to input SuSy spectra and decay tables calculated by the SPheno program into Pythia, for the purpose of determining whether experiments at the LHC will be sensitive to a connection between neutrino mixing and SuSy with bilinear Lepton Number violation. For the case of Baryon Number violation, an extension of the Lund String Model to colour topologies involving explicit Baryon Numbers is developed, called `junction fragmentation', which enables us to trace the migration of the generated Baryon number in some detail. This model is then itself extended and applied to improve the description of the physics of beam remnants in hadron--hadron collisions, in the context of a new framework for minimum-bias and underlying events. Based on the idea of multiple perturbative parton--parton interactions in hadron collisions, a sophisticated treatment of correlations in impact parameter, flavour, momentum, and colour is introduced. The last step so far in the evolution of this model has been the development of pT-ordered initial- and final-state radiation algorithms, which among other things have enabled us to `interleave' initial-state radiation and multiple interactions into one common sequence of decreasing pT values. Nonetheless, there is still a long way to go before a clear and unique picture of hadron collisions may emerge.
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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 | 0.002 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".