Measurement of high-$Q^2$ neutral current cross-sections with longitudinally polarised positrons with the ZEUS detector
Bibliographic record
Abstract
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 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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".