Measurement of suppression of large-radius jets and its dependence on substructure in Pb+Pb collisions at $\\sqrt{s_\\mathrm{NN}} = 5.02$ TeV with the ATLAS detector
Bibliographic record
Abstract
This letter presents a measurement of the nuclear modification factor of large-radius jets in $\\sqrt{s_\\mathrm{NN}} = 5.02$ TeV Pb+Pb collisions by the ATLAS experiment. The measurement is performed using 1.72 nb$^{-1}$ and 257 pb$^{-1}$ of Pb+Pb and $pp$ data, respectively. The large-radius jets are reconstructed with the anti-$k_{t}$ algorithm using a radius parameter of $R = 1.0$, by re-clustering anti-$k_{t}$$R = 0.2$ jets, and are measured over the kinematic range of $158 < p_{\\mathrm{T}} < 1000$ GeV and $|y|<2.0$. The large-radius jet constituents are further re-clustered using the $k_{t}$ algorithm in order to obtain the splitting parameters, $\\sqrt{d_{12}}$ and $\\Delta R_{12}$, which characterize the transverse momentum scale and angular separation for the hardest splitting in the jet, respectively. The nuclear modification factor, $R_{\\mathrm{AA}}$, obtained by comparing the Pb+Pb jet yields to those in $pp$ collisions, is measured as a function of jet transverse momentum ($p_{\\mathrm{T}}$) and $\\sqrt{d_{12}}$ or $\\Delta R_{12}$. A significant difference in the quenching of large-radius jets having single sub-jet and those with more complex substructure is observed. Systematic comparison of jet suppression in terms of $R_{\\mathrm{AA}}$ for different jet definitions is also provided. Presented results support the hypothesis that jets with hard internal splittings lose more energy through quenching and provide a new perspective for understanding the role of jet structure in jet suppression.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".