Event generation at MEPS@NLO accuracy in neutral and charged current DIS at the EIC
Bibliographic record
Abstract
We present state-of-the-art hadron-level predictions for the deep-inelastic scattering process at next-to-leading-order precision for several multiplicities, consistently merged in one sample. For the first time at this level of accuracy, we consider both neutral and charged current deep-inelastic scattering at the Electron-Ion Collider and present the first application of consistent next-to-leading-order merging to charged current deep-inelastic scattering in general. We critically examine inclusive predictions using multileg merging techniques, contrasting perturbative and nonperturbative uncertainties. Further, we study typical kinematic deep-inelastic scattering observables as well as jet measurements and 1-jettiness with realistic cuts implied by expected and past detector resolution. On the perturbative side, we see large corrections toward small virtualities and Bjorken- x , which can be captured by higher-multiplicity matrix elements and the merging procedure. Nonperturbative effects, while negligible in most jet observables, can reach similar size as the perturbative uncertainties around the peak of the 1-jettiness distributions especially at low values of Q 2 .
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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".