Investigating $$D^0$$ meson production in p-Pb collisions at 5.02 TeV with a multi-phase transport model
Bibliographic record
Abstract
Abstract We study the production of $$D^0$$ D 0 meson in p+p and p-Pb collisions using the improved AMPT model considering both coalescence and independent fragmentation of charm quarks after the Cronin broadening is included. After a detailed discussion of the improvements implemented in the AMPT model for heavy quark production, we show that the modified AMPT model can provide a good description of $$D^0$$ D 0 meson spectra in p-Pb collisions, the $$Q_{\textrm{pPb}}$$ Q pPb data at different centralities and $$R_{\textrm{pPb}}$$ R pPb data in both mid- and forward (backward) rapidities. We also studied the effects of nuclear shadowing and parton cascade on the rapidity dependence of $$D^{0}$$ D 0 meson production and $$R_{\textrm{pPb}}$$ R pPb . Our results indicate that using the same strength of the Cronin effect (i.e $$\delta $$ δ value) as that obtained from the mid-rapidity data leads to a considerable overestimation of the $$D^0$$ D 0 meson spectra and $$R_{\textrm{pPb}}$$ R pPb data at high $$p_{\textrm{T}}$$ p T in the backward rapidity. As a result, the $$\delta $$ δ is determined via a $$\chi ^2$$ χ 2 fitting of the $$R_{\textrm{pPb}}$$ R pPb data across various rapidities. This work lays the foundation for a better understanding of cold-nuclear-matter (CNM) effects in relativistic heavy-ion collisions.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 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".