Measurements of the suppression and correlations of dijets in Pb+Pb collisions at $\sqrt{s_{_\text{NN}}}$ = 5.02 TeV
Bibliographic record
Abstract
Studies of the correlations of the two highest transverse momentum (leading) jets in individual Pb+Pb collision events can provide information about the mechanism of jet quenching by the hot and dense matter created in such collisions. In Pb+Pb and pp collisions at $\sqrt{s_{_\text{NN}}}$ = 5.02 TeV, measurements of the leading dijet transverse momentum ($p_{\mathrm{T}}$) correlations are presented. Additionally, measurements in Pb+Pb collisions of the dijet pair nuclear modification factors projected along leading and subleading jet $p_{\mathrm{T}}$ are made. The measurements are performed using the ATLAS detector at the LHC with 260 pb$^{-1}$ of pp data collected in 2017 and 2.2 nb$^{-1}$ of Pb+Pb data collected in 2015 and 2018. An unfolding procedure is applied to the two-dimensional leading and subleading jet $p_{\mathrm{T}}$ distributions to account for experimental effects in the measurement of both jets. Results are provided for dijets with leading jet $p_{\mathrm{T}}$ greater than 100 GeV. Measurements of the dijet-yield-normalized $x_{\mathrm{J}}$ distributions in Pb+Pb collisions show an increased fraction of imbalanced jets compared to pp collisions; these measurements are in agreement with previous measurements of the same quantity at 2.76 TeV in the overlapping kinematic range. Measurements of the absolutely-normalized dijet rate in Pb+Pb and pp collisions are also presented, and show that balanced dijets are significantly more suppressed than imbalanced dijets in Pb+Pb collisions. It is observed in the measurements of the pair nuclear modification factors that the subleading jets are significantly suppressed relative to leading jets with $p_{\mathrm{T}}$ between 100 and 316 GeV for all centralities in Pb+Pb collisions.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".