Jet radius dependence of dijet momentum balance and suppression in Pb plus Pb collisions at 5.02 TeV with the ATLAS detector
Bibliographic record
Abstract
This paper describes a measurement of the jet radius dependence of the dijet momentum balance between leading back-to-back jets in \( 1.72 \text{nb}^{-1} \) of Pb+Pb collisions collected in 2018 and \( 255 \text{pb}^{-1} \) of \( p \cdot p \) collisions collected in 2017 by the ATLAS detector at the LHC. Both datasets were collected at \( \sqrt{s_{NN}} = 5.02 \) TeV. Jets are reconstructed using the anti-\( k_t \) algorithm with jet radius parameters \( R = 0.2, 0.3, 0.4, 0.5 \), and \( 0.6 \). The dijet momentum balance distributions are constructed for leading jets with transverse momentum \( p_T \) from 100 to 562 GeV for \( R = 0.2, 0.3 \), and \( 0.4 \) jets, and from 158 to 562 GeV for \( R = 0.5 \) and \( 0.6 \) jets. The absolutely normalized dijet momentum balance distributions are constructed to compare measurements of the dijet yields in Pb+Pb collisions directly to the dijet cross sections in \( p \cdot p \) collisions. For all jet radii considered here, there is a suppression of more balanced dijets in Pb+Pb collisions compared with \( p \cdot p \) collisions, while for more imbalanced dijets there is an enhancement. There is a jet radius dependence to the dijet yields, being stronger for more imbalanced dijets than for more balanced dijets. Additionally, jet pair nuclear modification factors are measured. The subleading jet yields are found to be more suppressed than leading jet yields in dijets. A jet radius dependence of the pair nuclear modification factors is observed, with the suppression decreasing with increasing jet radius. These measurements provide new constraints on jet quenching scenarios in the quark-gluon plasma.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".