Extracting the jet transport coefficient from jet quenching in high-energy heavy-ion collisions
Bibliographic record
Abstract
Within five different approaches to parton propagation and energy loss in dense matter, a phenomenological study of experimental data on suppression of large-${p}_{T}$ single inclusive hadrons in heavy-ion collisions at both the BNL Relativistic Heavy Ion Collider (RHIC) and the CERN Large Hadron Collider (LHC) was carried out. The evolution of bulk medium used in the study for parton propagation was given by 2 + 1 dimensional or 3 + 1 dimensional hydrodynamic models which are also constrained by experimental data on bulk hadron spectra. Values for the jet transport parameter $\stackrel{\ifmmode \hat{}\else \^{}\fi{}}{q}$ at the center of the most central heavy-ion collisions are extracted or calculated within each model, with parameters for the medium properties that are constrained by experimental data on the hadron suppression factor ${R}_{AA}$. For a quark with initial energy of 10 GeV we find that $\stackrel{\ifmmode \hat{}\else \^{}\fi{}}{q}\ensuremath{\approx}1.2\ifmmode\pm\else\textpm\fi{}0.3$ GeV${}^{2}$/fm at an initial time ${\ensuremath{\tau}}_{0}=0.6$ fm/$c$ in Au + Au collisions at $\sqrt{s}=200$ GeV/n and $\stackrel{\ifmmode \hat{}\else \^{}\fi{}}{q}\ensuremath{\approx}1.9\ifmmode\pm\else\textpm\fi{}0.7$ GeV${}^{2}$/fm in Pb + Pb collisions at $\sqrt{s}=2.76$ TeV/n. Compared to earlier studies, these represent significant convergence on values of the extracted jet transport parameter due to new constraints provided by recent experiment data from the LHC.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".