Inclusive jet and hadron suppression in a multistage approach
Bibliographic record
Abstract
We present a new study of jet interactions in the quark-gluon plasma created in high-energy heavy-ion collisions, using a multistage event generator within the jetscape framework. We focus on medium-induced modifications in the rate of inclusive jets and high transverse momentum (high-${p}_{\mathrm{T}}$) hadrons. Scattering-induced jet energy loss is calculated in two stages: a high virtuality stage based on the matter model, in which scattering of highly virtual partons modifies the vacuum radiation pattern, and a second stage at lower jet virtuality based on the lbt model, in which leading partons gain and lose virtuality by scattering and radiation. Coherence effects that reduce the medium-induced emission rate in the matter phase are also included. The trento model is used for initial conditions, and the ($2+1$)dimensional vishnu model is used for viscous hydrodynamic evolution. Jet interactions with the medium are modeled via 2-to-2 scattering with Debye screened potentials, in which the recoiling partons are tracked, hadronized, and included in the jet clustering. Holes left in the medium are also tracked and subtracted to conserve transverse momentum. Calculations of the nuclear modification factor (${R}_{\mathrm{AA}}$) for inclusive jets and high-${p}_{\mathrm{T}}$ hadrons are compared to experimental measurements at the BNL Relativistic Heavy Ion Collider (RHIC) and the CERN Large Hadron Collider (LHC). Within this framework, we find that with one extra parameter which codifies the transition between stages of jet modification---along with the typical parameters such as the coupling in the medium, the start and stop criteria, etc.---we can describe these data at all energies for central and semicentral collisions without a rescaling of the jet transport coefficient $\stackrel{\ifmmode \hat{}\else \^{}\fi{}}{q}$.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".