Reweighting Method for Lund String Breaks in PYTHIA
Bibliographic record
Abstract
Event generators are useful for simulating collision experiments in high-energy particle physics. In the event generator PYTHIA 8, parameters may be varied to compare competing models against experimental data. It is then beneficial to employ reweighting techniques to explore the results for multiple parameter values with only one simulation. This study aims to develop and implement a reweighting method for meson production in electron-positron collisions, within the Lund string model for hadronization. We expect the number of ss¯, uu¯ and dd¯ string breaks to follow a multinomial distribution and the produced mesons to distribute correspondingly. Thus, a ratio between two multinomial mass functions using the string break probabilities for two different sets of parameters is developed and used as statistical weight for each event. By varying the s ¯ s suppression parameter as well as η and η′ rejection parameters the weight for each event is calculated and applied to the distribution of final s ¯ s breaks and mesons from a set of test simulations. The reweighted test distributions are compared to target distributions for comparison. The results show a high accuracy of the method when applied to the number of string breaks but much lower for the number of final mesons, pointing towards a discrepancy between the predicted correlation of string breaks and mesons and the true correlation. The reweighting technique introduced can be naturally extended to reweighting around the mixing angles of the pseudoscalar and vector mesons as well as the vector-topseudoscalar suppression factor also present. It can also be further generalised to include baryons and account for hadron decays. In the end, one hopes to use this method for comparison with experimental data.
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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 | 0.003 | 0.015 |
| 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 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.013 | 0.005 |
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".