Measurements of W+W−+ ≥ 1 jet production cross-sections in pp collisions at $$ \sqrt{s} $$ = 13 TeV with the ATLAS detector

Fiducial and differential cross-section measurements of W+W− production in association with at least one hadronic jet are presented. These measurements are sensitive to the properties of electroweak-boson self-interactions and provide a test of perturbative quantum chromodynamics and the electroweak theory. The analysis is performed using proton-proton collision data collected at $$ \sqrt{s} $$ s = 13 TeV with the ATLAS experiment, corresponding to an integrated luminosity of 139 fb−1. Events are selected with exactly one oppositely charged electron-muon pair and at least one hadronic jet with a transverse momentum of pT> 30 GeV and a pseudorapidity of |η| < 4.5. After subtracting the background contributions and correcting for detector effects, the jet-inclusive W+W−+ ≥ 1 jet fiducial cross-section and W+W−+ jets differential cross-sections with respect to several kinematic variables are measured. These measurements include leptonic quantities, such as the lepton transverse momenta and the transverse mass of the W+W− system, as well as jet-related observables such as the leading jet transverse momentum and the jet multiplicity. Limits on anomalous triple-gauge-boson couplings are obtained in a phase space where interference between the Standard Model amplitude and the anomalous amplitude is enhanced.

Parametrized classifiers for optimal EFT sensitivity

We study unbinned multivariate analysis techniques, based on Statistical Learning, for indirect new physics searches at the LHC in the Effective Field Theory framework. We focus in particular on high-energy ZW production with fully leptonic decays, modeled at different degrees of refinement up to NLO in QCD. We show that a considerable gain in sensitivity is possible compared with current projections based on binned analyses. As expected, the gain is particularly significant for those operators that display a complex pattern of interference with the Standard Model amplitude. The most effective method is found to be the “Quadratic Classifier” approach, an improvement of the standard Statistical Learning classifier where the quadratic dependence of the differential cross section on the EFT Wilson coefficients is built-in and incorporated in the loss function. We argue that the Quadratic Classifier performances are nearly statistically optimal, based on a rigorous notion of optimality that we can establish for an approximate analytic description of the ZW process.