Speaker
Description
SMEFT is a powerful framework to explore imprint of New Physics when the mass of the new states is out of experimental reach. In recent years, significant effort has been devoted to performing global fits in order to correlate deviations observed across different measurements. However, the large dimensionality of the parameter space can limit the sensitivity of this approach and obfuscate the identification of potential signals. In this talk, I will discuss how to move beyond traditional global fits by adopting a Bayesian model selection framework combined with genetic algorithms, enabling a more effective detection of deviations from the SM and a more accurate characterization of the underlying new physics scenario in the context of future lepton colliders.
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