Determining the jet transport coefficient <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mover accent="true"><mml:mi>q</mml:mi><mml:mo></mml:mo></mml:mover></mml:math> from inclusive hadron suppression measurements using Bayesian parameter estimation
Bibliographic record
Abstract
We report a new determination of $\stackrel{\ifmmode \hat{}\else \^{}\fi{}}{q}$, the jet transport coefficient of the quark-gluon plasma. We use the JETSCAPE framework, which incorporates a novel multistage theoretical approach to in-medium jet evolution and Bayesian inference for parameter extraction. The calculations, based on the Matter and Lbt jet quenching models, are compared to experimental measurements of inclusive hadron suppression in $\mathrm{Au}+\mathrm{Au}$ collisions at the BNL Relativistic Heavy Ion Collider (RHIC) and $\mathrm{Pb}+\mathrm{Pb}$ collisions at the CERN Large Hadron Collider (LHC). The correlation of experimental systematic uncertainties is accounted for in the parameter extraction. The functional dependence of $\stackrel{\ifmmode \hat{}\else \^{}\fi{}}{q}$ on jet energy or virtuality and medium temperature is based on a perturbative picture of in-medium scattering, with components reflecting the different regimes of applicability of Matter and Lbt. In the multistage approach, the switch between Matter and Lbt is governed by a virtuality scale ${Q}_{0}$. Comparison of the posterior model predictions to the RHIC and LHC hadron suppression data shows reasonable agreement, with moderate tension in limited regions of phase space. The distribution of $\stackrel{\ifmmode \hat{}\else \^{}\fi{}}{q}/{T}^{3}$ extracted from the posterior distributions exhibits weak dependence on jet momentum and medium temperature $T$, with 90% credible region (CR) depending on the specific choice of model configuration. The choice of Matter$+$Lbt, with switching at virtuality ${Q}_{0}$, has 90% CR of $2<\stackrel{\ifmmode \hat{}\else \^{}\fi{}}{q}/{T}^{3}<4$ for ${p}_{T,\mathrm{jet}}>40 \mathrm{GeV}/c$. The value of ${Q}_{0}$, determined here for the first time, is in the range 2.0--2.7 GeV.
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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 | 0.000 |
| 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".