Speaker
Description
Neural Simulation-Based Inference (NSBI) is an analysis technique which leverages the output of trained deep neural networks (DNNs) to construct a surrogate likelihood ratio which can then be used for a binned or unbinned likelihood scan. These techniques have show some success when applied to analyses involving effective field theory (EFT) approaches, where it can be difficult to achieve sensitivity using a hand-engineered variable to infer the likelihood. In this talk, we will report the recent progress in implementing NSBI techniques to CMS data analyses involving top pair production. In particular, we will explore the challenges involved with implementing NSBI in analyses involving many parameters of interest, such as the Wilson coefficients in an EFT analysis. We will also report on the various approaches used in incorporating systematic uncertainties in NSBI analyses.