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.
Author
Jason Aebischer
(University of Zurich)
