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Record W4412259943

Measurements of normalized differential cross sections for t ¯ t production in p p collisions at √ ( s ) = 7     TeV using the ATLAS detector

2014· article· en· W4412259943 on OpenAlexaff
G. Aad, B. Abbott, J. Abdallah, S. Abdel Khalek, O. Abdinov

Bibliographic record

VenueAmericanae (AECID Library) · 2014
Typearticle
Languageen
FieldPhysics and Astronomy
TopicParticle Detector Development and Performance
Canadian institutionsInstitute of Particle Physics
Fundersnot available
KeywordsAtlas detectorAtlas (anatomy)PhysicsNuclear physicsDetectorDifferential (mechanical device)Particle physicsProduction (economics)Large Hadron ColliderGeologyOpticsEconomics
DOInot available

Abstract

fetched live from OpenAlex

Measurements of normalized differential cross-sections for top-quark pair production are presentedas a function of the top-quark transverse momentum, and of the mass, transverse momentum, and√rapidity of the t t ̄ system, in proton?proton collisions at a center-of-mass energy of s = 7 TeV . Thedataset corresponds to an integrated luminosity of 4.6 fb −1 , recorded in 2011 with the ATLAS detectorat the CERN Large Hadron Collider. Events are selected in the lepton+jets channel, requiring exactlyone lepton and at least four jets with at least one of the jets tagged as originating from a b -quark.The measured spectra are corrected for detector efficiency and resolution effects and are comparedto several Monte Carlo simulations and theory calculations. The results are in fair agreement withthe predictions in a wide kinematic range. Nevertheless, data distributions are softer than predictedfor higher values of the mass of the t t ̄ system and of the top-quark transverse momentum. Themeasurements can also discriminate among different sets of parton distribution functions.

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)0.001
metaresearch head score (Gemma)0.001
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: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.004
Threshold uncertainty score0.012

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0020.002
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0040.001

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 head0.027
GPT teacher head0.264
Teacher spread0.237 · 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

Citations5
Published2014
Admission routes1
Has abstractyes

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Same venueAmericanae (AECID Library)Same topicParticle Detector Development and PerformanceFrench-language works237,207