  Vector boson fusion established itself as a highly reliable channel to probe
the Higgs boson and an avenue to uncover new physics at the Large Hadron
Collider. This channel provides the most stringent bound on Higgs' invisible
decay branching ratio, where the current upper limits are significantly higher
than the one expected in the Standard Model. It is remarkable that merely
low-level calorimeter data from this characteristically simple process can
improve this limit substantially by employing sophisticated deep-learning
techniques. The construction of such neural networks seems to comprehend the
event kinematics and radiation pattern exceptionally well. However, the full
potential of this outstanding capability also warrants a precise theoretical
projection of QCD parton showering and corresponding radiation pattern. This
work demonstrates the relation using different recoil schemes in the parton
shower with leading order and higher-order computation.
