  Extracting longitudinal modes of weak bosons in LHC processes is essential to
understand the electroweak-symmetry-breaking mechanism. To that end, we propose
a general method, based on wide neural networks, to properly model
longitudinal-boson signals and hence enable the event-by-event tagging of
longitudinal bosons. It combines experimentally accessible kinematic
information and genuine theoretical inputs provided by amplitudes in
perturbation theory. As an application we consider the production of a Z boson
in association with a jet at the LHC, both at leading order and in the presence
of parton-shower effects. The devised neural networks are able to extract
reliably the longitudinal contribution to the unpolarised process. The proposed
method is very general and can be systematically extended to other processes
and problems.
