Bibliographic record
Abstract
The jet quenching or transverse diffusion coefficient, $\hat{q}=\langle(\Delta k_T)^2\rangle / L$, provides the primary metric that characterize the modification of hard jets in a QGP. A quantitative extraction of $\hat{q}$ from measurements requires sophisticated jet quenching theory, precise and extensive experimental data, as well as an advanced statistical framework that compares theory to data. Within the JETSCAPE collaboration we have developed a multi-stage approach of jet evolution, where the medium-modified parton showers at high virtuality scale are described using the DGLAP evolution and simulated with the MATTER event generator, while the in-medium elastic and inelastic scatterings of partons at low virtuality scale are described using a transport theory implemented with the LBT event generator. The transition from the DGLAP phase to the transport phase of jet modification, and its dependence on properties of the jet and the local medium, has previously only been approximately estimated. The goal of this work is to use the advanced statistical framework to determine this transition. To this end, a simplified version of the state-of-the-art JETSCAPE generator is embedded within a Bayesian analysis framework for a simultaneous calibration on jet quenching data at various centrality bins in 200~GeV Au-Au, 2.76~TeV Pb-Pb and 5.02~TeV Pb-Pb collisions, from which $\hat{q}$ is systematically extracted as functions of both jet energy and medium temperature. The dependence of $\hat{q}$ on the medium-induced virtuality scale is quantitatively explored for the first time. This virtuality, in combination with the local temperature and parton energy, serves as a crucial separation scale between the DGLAP region and the transport region for jet quenching. Its median value is determined to be 2.1 or 2.9~GeV for two different parameterizations of the jet quenching coefficient.
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".