A precise measurement of the jet energy scale derived from single-particle measurements and in situ techniques in proton-proton collisions at $\\sqrt{s}=$ 13 TeV with the ATLAS detector
Bibliographic record
Abstract
The jet energy calibration and its uncertainties are derived from measurements of the calorimeter response to single particles in both data and Monte Carlo simulation using proton-proton collisions at $\sqrt{s} = 13$ TeV collected with the ATLAS detector during Run 2 at the Large Hadron Collider. The jet calibration uncertainty for anti-k$_t$ jets with a jet radius parameter of $R = 0.4$ and in the central jet rapidity region is about 2.5% for transverse momenta ($p_T$) of 20 GeV, about 0.5% for $p_T = 300$ GeV and 0.7% for $p_T = 4$ TeV. Excellent agreement is found with earlier determinations obtained from $p_T$-balance based in situ methods ($Z/\gamma$+jets). The combination of these two independent methods results in the most precise jet energy measurement achieved so far with the ATLAS detector with a relative uncertainty of 0.3% at $p_T = 300$ GeV and 0.6% at $4$ TeV. The jet energy calibration is also derived with the single-particle calorimeter response measurements separately for quark- and gluon-induced jets and furthermore for jets with $R$ varying from 0.2 to 1.0 retaining the correlations between these measurements. Differences between inclusive jets and jets from boosted top-quark decays, with and without grooming the soft jet constituents, are also studied.
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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 | 0.001 |
| 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 | 0.001 | 0.001 |
| 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".