Factorizing Charm Production in Proton-Proton Collisions with PYTHIA
Bibliographic record
Abstract
In this thesis, proton-proton collisions at 7 TeV are simulated using PYTHIA. Charm and anticharm quarks are identified as being produced from direct, meaning that the charm and anticharm are pair produced by a hard scattering, or indirect interactions where the charm and anticharm pair is produced from the splitting of a single gluon. This data is used to generate a pT spectrum and correlation functions for rapidity and the azimuthal angle. The relative contributions of the direct and indirect processes to the histograms are then tuned to better match the experimental data taken from the LHCb. The goal is to obtain a more accurate fraction of direct to indirect interactions that produce charm quarks in proton-proton collisions. The direct to indirect production fraction was determined to be 0.54, which is significantly lower than the fraction estimated by PYTHIA, as that was 1.28. The overall shapes of the correlation functions and pT spectra did follow the LHCb data; however, for small values of |∆ϕ| and |∆y|, the correlation functions had lower magnitudes compared to the experimental data. Furthermore, the simulated PYTHIA data created more charm and anticharm quarks at low pT compared to what was measured in the LHCb, causing some of the discrepancy. This suggests that further refinement is needed to make the simulation more accurate to model charm and anticharm production from proton-proton collisions properly.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".