DYTurbo: Fast predictions for Drell–Yan processes
Bibliographic record
Abstract
Drell–Yan lepton pair production processesare extremely important for Standard Model (SM) pre-cision tests and for beyond the SM searches at hadroncolliders. Fast and accurate predictions are essential toenable the best use of the precision measurements ofthese processes; they are used for parton density fits, forthe extraction of fundamental parameters of the SM, andfor the estimation of background processes in searches.This paper describes a new numerical program,DYTurbo,for the calculation of the QCD transverse-momentumresummation of Drell–Yan cross sections up to next-to-next-to-leading logarithmic accuracy combined withthe fixed-order results at next-to-next-to-leading order(O(α2S)), including the full kinematical dependence ofthe decaying lepton pair with the corresponding spincorrelations and the finite-width effects. TheDYTurboprogram is an improved reimplementation of theDYqT,DYResandDYNNLOprograms, which provides fast andnumerically precise predictions through the factorisationof the cross section into production and decay variables,and the usage of quadrature rules based on interpolatingfunctions for the integration over kinematic variables.
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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.015 | 0.005 |
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".