To Profile or To Marginalize -- A SMEFT Case Study

Abstract

Global SMEFT analyses have become a key interpretation framework for LHC physics, quantifying how well a large set of kinematic measurements agrees with the Standard Model. This agreement is encoded in measured Wilson coefficients and their uncertainties. A technical challenge of global analyses are correlations. We compare, for the first time, results from a profile likelihood and a Bayesian marginalization for a given data set with a comprehensive uncertainty treatment. Using the validated Bayesian framework we analyse a series of new kinematic measurements. For the updated dataset we find and explain differences between the marginalization and profile likelihood treatments.

38 pages, 27 figures

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• Froggatt-Nielsen Meets the SMEFT
• Staying on Top of SMEFT-Likelihood Analyses
• Higgs couplings in SMEFT via Zh production at the HL-LHC
• Probing Z/W Pole Physics at High-energy Muon Colliders via Vector-boson-fusion Processes
• Inferring correlated distributions: boosted top jets
• Tracing the bottom electroweak dipole operators at future lepton colliders