Higher-order effects in amplitude-assisted polarisation extraction with machine-learning techniques
This paper presents the first amplitude-assisted regression procedure at next-to-leading-order QCD accuracy, augmented with parton-shower effects and machine-learning techniques, to robustly extract the longitudinal-boson production rate in di-boson collisions at the LHC.