CHIPP 2024 Annual meeting

Machine Learning in b -> s ll

19 Jun 2024, 15:35

12m

MR060

ML Workshop

Speaker

Jason Aebischer (University of Zurich)

Description

Short-distance (SD) effects in $b\to s{l}^{+}{l}^{-}$ transitions can give large corrections to the Standard Model prediction. They can however not be computed from first principles. In my talk I will present a neural network, that takes such SD effects into account, when inferring the Wilson coefficients $C_9$ and $C_{10}$ from $b\to s{l}^{+}{l}^{-}$ angular observables. The model is based on likelihood-free inference and allows to put stronger bounds on new phyiscs scenarios than conventional global fits.

Presentation materials

1_CHIPP_Aebischer.pdf