A fast and differentiable SMEFT likelihood

About

The Standard Model Effective Field Theory (SMEFT) has proven to be a valuable framework for studying a broad class of models beyond the SM containing heavy degrees of freedom. The phenomenology of SMEFT can be developed systematically, incorporating essential effects such as Renormalization Group (RG) evolution and matching onto subsequent low-energy Weak Effective Field Theories (WET), enabling predictions for a wide range of observables. A likelihood function for the SMEFT that compares theory predictions to experimental data can be employed to identify patterns of deviation from SM predictions, but also to study the phenomenology of any specific model that can be matched onto SMEFT. In this talk, we present a new major version of the Python package smelli - a SMEFT likelihood, which incorporates a large number of observables, ranging from quark and lepton flavor physics, to Higgs physics, electroweak precision observables, beta decays, and high-mass Drell-Yan tails. It provides a fast and analytically differentiable global flavorful likelihood function for the SMEFT, WET, and for new physics models, with no assumptions on the flavor structure..

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