Leading order, next-to-leading order, and non-perturbative parton collision kernels: Effects on the jet substructure
Bibliographic record
Abstract
As an important signature of the quark-gluon plasma (QGP), a highprecision energy-loss model is essential to independently verify the QGP properties extracted from soft particles. In this work, we optimize the energy loss modeling in MARTINI by introducing the formation time of the parton shower in the initial hard scattering, which is essential for a simultaneous description of the hadron and jet R AA . Based on this, we study the phenomenological influence of the higher order collision kernels—the up-to-NLO one evaluated by EQCD and the non-perturbative (NP) one computed in lattice QCD — on the energy loss of the hard parton, compared to the LO kernel. The hadron and jet R AA are calculated with AMY rates using the three kernels and the optimized parameter sets for the running coupling. The results exhibit remarkable similarities in their overall values as well as in p T and centrality dependence. Sizable differences in the jet substructure are observed between different soft radiation rates.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".