Speaker
Description
The Standard Model Effective Field Theory (SMEFT) provides a systematic, model-independent extension of the Standard Model through higher-dimensional operators, enabling collider data to be interpreted in the presence of heavy new physics. In recent years, global SMEFT analyses have achieved substantial progress in constraining the parameter space, yet their discovery potential remains limited by the problem’s high dimensionality and by the requirement to fit all operators simultaneously. In this talk, I will present a novel strategy that re-casts SMEFT fits into an optimised, discovery-oriented framework. The approach combines Bayesian model selection with genetic algorithms to efficiently navigate the space of operator subsets, identifying deformations that improve agreement with data while minimising model complexity. I will demonstrate the method using current LHC and LEP measurements as well as future collider projections, and validate its performance through closure tests with injected UV signals. The results show that SMEFT, when equipped with model-selection techniques, can act as a genuine discovery tool, pointing to the regions of parameter space where indirect signs of new physics are most likely to emerge.