Renormalization-group improved fully differential cross sections for top pair production
A. Broggio, A. S. Papanastasiou, A. Signer
TL;DR
The paper advances top-quark pair production predictions by incorporating semi-leptonic decays within a narrow-width, fully differential framework using RG-improved NNLL resummation to produce approximate NNLO production kernels. Implemented in a flexible parton-level Monte Carlo, the approach compares PIM and 1PI kinematics to study a broad class of IR-safe observables and provides a robust error framework that combines scale variation with kinematic envelope. Key findings show that approximate NNLO predictions often align well with full NLO for many observables and reduce theoretical uncertainties, though missing delta-function pieces and phase-space approximations can cause a modest underprediction relative to full NNLO for total cross sections. The results lay out a practical, differential tool for current and future collider analyses, while outlining concrete avenues for achieving full NNLO accuracy, including improved decay corrections and off-shell effects.
Abstract
We extend approximate next-to-next-to-leading order results for top-pair production to include the semi-leptonic decays of top quarks in the narrow-width approximation. The new hard-scattering kernels are implemented in a fully differential parton-level Monte Carlo that allows for the study of any IR-safe observable constructed from the momenta of the decay products of the top. Our best predictions are given by approximate NNLO corrections in the production matched to a fixed order calculation with NLO corrections in both the production and decay subprocesses. Being fully differential enables us to make comparisons between approximate results derived via different (PIM and 1PI) kinematics for arbitrary distributions. These comparisons reveal that the renormalization-group framework, from which the approximate results are derived, is rather robust in the sense that applying a realistic error estimate allows us to obtain a reliable prediction with a reduced theoretical error for generic observables and analysis cuts.
