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Record W2602950463 · doi:10.3204/desy-2014-01481

Measurement of high-$Q^2$ neutral current cross-sections with longitudinally polarised positrons with the ZEUS detector

2022· dissertation· en· W2602950463 on OpenAlexaboutno aff
Trevor Stewart

Bibliographic record

VenueDESY (CERN, DESY, Fermilab, IHEP, and SLAC) · 2022
Typedissertation
Languageen
FieldPhysics and Astronomy
TopicParticle Detector Development and Performance
Canadian institutionsnot available
FundersDeutsches Elektronen-Synchrotron
KeywordsZEUS (particle detector)Neutral currentCurrent (fluid)PhysicsNuclear physicsPositronDetectorCharged currentParticle physicsOpticsDeep inelastic scatteringScatteringNeutrinoElectron

Abstract

fetched live from OpenAlex

Measurement of High-Q2 Neutral Current Cross-sections with Longitudinally Polarised Positrons with the ZEUS DetectorTrevor P. StewartDoctor of PhilosophyGraduate Department of Department of PhysicsUniversity of Toronto2012The cross sections for neutral current (NC) deep inelastic scattering (DIS) in e+ p collisions with a longitudinally polarised positron beam are measured at high momentumtransfer squared (Q2>185 GeV2) at the ZEUS detector at HERA. The HERA accelerator provides e p collisions at a centre-of-mass energy of 318 GeV, which allows the weak contribution to the NC process to be studied at highQ2. The measurements are based on a data sample with an integrated luminosity of 135:5 pb1collected with the ZEUS detector in 2006 and 2007. The single dierential NC cross sectionsd=dQ2,d=dx and d=dy and the reduced cross section ~ are measured. The structure function x ~ F3 is determined by combining the e+p NC reduced cross sections with the previously measured ep[1] measurements. The interference structure function xF Z3 is extracted at Q2 = 1500 GeV2.The cross-section asymmetry between the positive and negative polarisation ofthe positron beam is measured and the parity violation eects of the electroweakinteraction are observed.The predictions of the Standard Model of particle physics agree well with the measurements.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.036
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.016
GPT teacher head0.253
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 teacher head, not a consensus.

Study designObservational
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
Published2022
Admission routes1
Has abstractyes

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