Rapidity Correlation of K/π to ϕ in γSCSM and Pythia
Bibliographic record
Abstract
Pythia with Rope Hadronization, and the Canonical-Statistical Model with strangeness undersaturation (γSCSM), have shown remarkably similar results when simulating strange hadrons yields relative to pion yields as a function of event-multiplicity. As the underlying assumptions in these models are very different, a novel observable is warranted. The aim of this thesis is to show that K0S/(π+ + π−) in ϕ-triggered events is an observable which distinguishes between Pythia and γSCSM. For this purpose K0S/(π+ + π−) in three rapidity windows w.r.t ϕ was studied in proton-proton collisions, namely, |∆y| < 0.1, |∆y| < 0.5 and |∆y| < 1 as a function of event multiplicity. With these considerations, stark differences were observed between the two models. The Lund String Model implemented with Pythia conserves strangeness on the level of string breaks, which results in larger K0S yields relative to π+ + π− in regions closer to ϕ. Contrary to this, the statistical nature of γSCSM does not predict any increase of K0/π in ϕ-triggered events when compared to non-triggered events. Moreover, Pythia events were generated for multiple values of the vector-meson mixing angle (θV ). This allowed for a study of ϕ/π based on the probability of ss¯, uu¯ or d¯d projecting onto ϕ, and how that affects K0S/(π+ +π−) in the different rapidity windows. Here it was observed that larger deviations from ideal-mixing(θV = 35.3◦) resulted in larger ϕ/π, whilst only minimal decreases in K0S/(π+ + π−) were noticed.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".