Measurement of jet production in deep inelastic scattering and NNLO determination of the strong coupling at ZEUS
Bibliographic record
Abstract
Abstract A new measurement of inclusive-jet cross sections in the Breit frame in neutral current deep inelastic scattering using the ZEUS detector at the HERA collider is presented. The data were taken in the years 2004–2007 at a centre-of-mass energy of $$318\,\,\textrm{GeV}$$ 318 GeV and correspond to an integrated luminosity of $$347\,\,\textrm{pb}^{-1}$$ 347 pb - 1 . The jets were reconstructed using the $$k_t$$ k t -algorithm in the Breit reference frame. They have been measured as a function of the squared momentum transfer, $$Q^2$$ Q 2 , and the transverse momentum of the jets in the Breit frame, $$p_{\perp ,\text {Breit}}$$ p ⊥ , Breit . The measured jet cross sections are compared to previous measurements and to perturbative QCD predictions. The measurement has been used in a next-to-next-to-leading-order QCD analysis to perform a simultaneous determination of parton distribution functions of the proton and the strong coupling, resulting in a value of $$\alpha _s(M_Z^2) = 0.1142 \pm 0.0017 \text {~(experimental/fit)}$$ α s ( M Z 2 ) = 0.1142 ± 0.0017 (experimental/fit) $${}_{-0.0007}^{+0.0006} \text {~(model/parameterisation)}$$ - 0.0007 + 0.0006 (model/parameterisation) $${}_{-0.0004}^{+0.0006} \text {~(scale)}$$ - 0.0004 + 0.0006 (scale) , whose accuracy is improved compared to similar measurements. In addition, the running of the strong coupling is demonstrated using data obtained at different scales.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
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".