Speaker
Nikita Schmal
Description
Global SMEFT analyses have become a key interpretation framework for LHC physics, quantifying how well a large set of kinematic measurements agrees with the Standard Model. We show how normalizing flows can be used to accelerate sampling from the SMEFT likelihood. The networks are trained without a pre-generated dataset by combining neural importance sampling with Markov chain methods. Furthermore, we use GPUs for fast evaluation of the likelihood, and compute profile likelihoods efficiently using differentiability.
| Track | Theory |
|---|
