A comprehensive framework for studying W′ and Z′ bosons at hadron colliders with automated jet veto resummation
Bibliographic record
Abstract
The production of high-mass, color-singlet particles in hadron collisions is universally accompanied by initial state QCD radiation that is predominantly soft with respect to the hard process scale Q and/or collinear with respect to the beam axis. At TeV-scale colliders, this is in contrast to top quark and multijet processes, which are hard and central. Consequently, vetoing events with jets possessing transverse momenta above pTVeto in searches for new color-singlet states can efficiently reduce non-singlet backgrounds, thereby increasing experimental sensitivity. To quantify this generic observation, we in-vestigate the production and leptonic decay of a Sequential Standard Model W′ boson at the 13 TeV Large Hadron Collider. We systematically consider signal and background processes at next-to-leading-order (NLO) in QCD with parton shower (PS) matching. For color-singlet signal and background channels, we resum Sudakov logarithms of the form αsj(pTVeto) logk(Q/pTVeto) up to next-to-next-to-leading logarithmic accuracy (NNLL) with NLO matching. We obtain our results using the MadGraph5_aMC@NLO and MadGraph5_aMC@NLO-SCET frameworks, respectively. Associated Universal Feyn-Rules Output model files capable of handling NLO+PS- and NLO+NNLL-accurate computations are publicly available. We find that within their given uncertainties, both the NLO+PS and NLO+NNLL(veto) calculations give accurate and consistent predictions. Consequently, jet vetoes applied to color-singlet processes can be reliably modeled at the NLO+PS level. With respect to a b-jet veto of pTVeto = 30 GeV, flavor-agnostic jet vetoes of pTVeto = 30 − 40 GeV can further reduce single top and tt¯tt¯ rates by a factor of 2-50 at a mild cost of the signal rate. Jet vetoes can increase the signal-to-noise ratios by roughly 10% for light W′ boson masses of 30 − 50 GeV and 25%-250% for masses of 300-800 GeV.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".