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Record W6901863679 · doi:10.60692/ssjen-k7344

Muon reconstruction performance of the ATLAS detector in proton–proton collision data at $$\sqrt{s}$$ s =13 TeV

2016· article· en· W6901863679 on OpenAlexaff

Bibliographic record

VenueGreater South Information System · 2016
Typearticle
Languageen
FieldPhysics and Astronomy
TopicParticle physics theoretical and experimental studies
Canadian institutionsUniversité de MontréalInstitute of Particle PhysicsUniversity of Victoria
Fundersnot available
KeywordsLarge Hadron ColliderMuonAtlas (anatomy)CollisionDetectorMomentum (technical analysis)Phase space

Abstract

fetched live from OpenAlex

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 $$0.2~\%$$ .

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.010
Threshold uncertainty score0.020

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0020.001

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.

Opus teacher head0.026
GPT teacher head0.221
Teacher spread0.195 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2016
Admission routes1
Has abstractyes

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