New techniques for jet calibration with the ATLAS detector
Bibliographic record
Abstract
A determination of the jet energy scale is presented using proton–proton collision data with a centre-ofmass energy of √s = 13 TeV, corresponding to an integrated \nluminosity of 140 fb−1 collected using the ATLAS detector \nat the LHC. Jets are reconstructed using the ATLAS particleflow method that combines charged-particle tracks and topoclusters formed from energy deposits in the calorimeter cells. \nThe anti-kt jet algorithm with radius parameter R = 0.4 is \nused to define the jet. Novel jet energy scale calibration strategies developed for the LHC Run 2 are reported that lay the \nfoundation for the jet calibration in Run 3. Jets are calibrated \nwith a series of simulation-based corrections, including stateof-the-art techniques in jet calibration such as machine learning methods and novel in situ calibrations to achieve better \nperformance than the baseline calibration derived using up \nto 81 fb−1 of Run 2 data. The performance of these new \ntechniques is then examined in the in situ measurements by \nexploiting the transverse momentum balance between a jet \nand a reference object. The b-quark jet energy scale using \nparticle flow jets is measured for the first time with around \n1% precision using γ +jet events.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".