Total Drell-Yan in the flavorful SMEFT
Gudrun Hiller, Lara Nollen, Daniel Wendler

TL;DR
This paper performs a comprehensive global analysis of Drell-Yan processes within the SMEFT framework, combining multiple observables to tighten constraints on new physics couplings using LHC data and projections for future colliders.
Contribution
It introduces a combined analysis of Drell-Yan observables in SMEFT, improving limits on couplings and demonstrating the benefits of a global approach over individual measurements.
Findings
Limits on first and second generation quark couplings exceed 10 TeV.
Combining flavor and high-$p_T$ data improves constraints on dipole operators by up to a factor of three.
Future colliders could enhance sensitivity by factors of 1.5 to 8.
Abstract
We perform a global analysis of Drell-Yan production of charged leptons and dineutrinos, the latter in missing energy plus jet events, in proton-proton collisions within the Standard Model Effective Field Theory (SMEFT). The combination allows for the removal of flat directions, sharper limits and to probe more couplings than the individual observables, which we show performing a fit to LHC-data. We also find that limits have only mild dependence on lepton flavor patterns; hierarchies in quark flavors are driven by the parton distribution functions. The strongest constraints are on couplings involving the first and second generation quarks, exceeding 10 TeV. Combining flavor and high- data, the limits on electroweak and gluon dipole operators can be improved, by up to a factor of three, highlighting once more that a more global approach increases sensitivities significantly. We…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
Taxonomy
TopicsParticle physics theoretical and experimental studies · Computational Physics and Python Applications · Neutrino Physics Research
