Deciphering yield modification of hadron-triggered semi-inclusive recoil jets in heavy-ion collisions
Bibliographic record
Abstract
Jet quenching is recognized as critical evidence for the existence of the quark-gluon plasma (QGP) and serves as an essential probe to study its transport properties. Measurements of hadron-triggered semi-inclusive recoil jets have gained popularity due to its capability to probe jets over an extended phase space at low transverse momenta ( p T ) and large radii. Recent ALICE measurements showed that the I AA , yield ratio of recoil jets between heavy-ion and p+p collisions, rises with jet p T and exceeds unity at high p T , contradicting conventional expectations that jet quenching should result in I AA values less than one. In this contribution, we re-examine the surface bias and study the effects of energy losses for both trigger hadrons and recoil jets on I AA , employing the Linear Boltzmann Transport (LBT) model to simulate jet-medium interactions. Our findings suggest that a large portion of hadrons used for the triggers undergoes substantial energy loss, despite surface bias. In particular, the energy loss of the trigger hadrons elevates the I AA baseline, corresponding to the case of no energy loss for recoil jets, to be greatly larger than unity. This enhancement of the baseline implies that the measured I AA values being larger than unity could still signal jet quenching.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".