Refinable modeling for unbinned SMEFT analyses - Robert Schöfbeck (Machine Learning: Science and Technology)
We present methods to estimate systematic uncertainties in unbinned large hadron collider (LHC) data analyses, focusing on constraining Wilson coefficients in the standard model effective field theory (SMEFT). Our approach also applies to broader parametric models of non-resonant phenomena beyond the standard model. By using machine-learned surrogates of the likelihood ratio, we extend well-established procedures from binned Poisson counting experiments to the unbinned case. This framework handles various theoretical, modeling, and experimental uncertainties, laying the foundation for future unbinned analyses at the LHC. We also introduce a tree-boosting algorithm that learns precise parametrizations of systematic effects, providing a robust, flexible alternative to neural networks for modeling systematics. We demonstrate this approach with an SMEFT analysis of highly energetic top quark pair production in proton–proton collisions.