Speaker
Description
SMEFT global analyses commonly encounter two major challenges:
1. An incomplete set of observables.
2. Ad-hoc flavour assumptions.
These issues significantly undermine the reliability and applicability of the results.
In this talk, I propose the CLEW framework for a complete global analysis free of flavour assumptions. By integrating both high- and low-energy data, our framework is able to apply strong phenomenological constraints instead of flavour assumptions to reduce the number of operators involved in the analysis.
Moreover, to aid in model building and guide experimental searches, CLEW utilizes the Akaike Information Criterion (AIC) to identify the most relevant operators. AIC helps select a group of operators that not only fit well with the experimental data but also avoid unnecessary complexity.
Finally, I will introduce the implementation of CLEW within SIMUnet, a machine-learning based tool that fits SMEFT and PDFs simultaneously.
