Muon reconstruction performance of the ATLAS detector in proton–proton collision data at $$\sqrt{s}$$ s =13 TeV
Bibliographic record
Abstract
This article documents the performance of the ATLAS muon identification and reconstruction using the LHC dataset recorded at $$\sqrt{s} = 13$$ TeV in 2015. Using a large sample of $$J/\psi \rightarrow \mu \mu $$ and $$Z\rightarrow \mu \mu $$ decays from 3.2 fb $$^{-1}$$ of pp collision data, measurements of the reconstruction efficiency, as well as of the momentum scale and resolution, are presented and compared to Monte Carlo simulations. The reconstruction efficiency is measured to be close to $$99~\%$$ over most of the covered phase space ( $$|\eta |<2.5$$ and $$5 < p_{\mathrm {T}} < 100$$ GeV). The isolation efficiency varies between 93 and $$100~\%$$ depending on the selection applied and on the momentum of the muon. Both efficiencies are well reproduced in simulation. In the central region of the detector, the momentum resolution is measured to be $$1.7~\%$$ ( $$2.3~\%$$ ) for muons from $$J/\psi \rightarrow \mu \mu $$ ( $$Z\rightarrow \mu \mu $$ ) decays, and the momentum scale is known with an uncertainty of $$0.05~\%$$ . In the region $$|\eta |>2.2$$ , the $$p_{\mathrm {T}} $$ resolution for muons from $$Z\rightarrow \mu \mu $$ decays is $$2.9~\%$$ while the precision of the momentum scale for low- $$p_{\mathrm {T}} $$ muons from $$J/\psi \rightarrow \mu \mu $$ decays is about $$0.2~\%$$ .
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".