  QCD splittings are among the most fundamental theory concepts at the LHC. We
show how they can be studied systematically with the help of invertible neural
networks. These networks work with sub-jet information to extract fundamental
parameters from jet samples. Our approach expands the LEP measurements of QCD
Casimirs to a systematic test of QCD properties based on low-level jet
observables. Starting with an toy example we study the effect of the full
shower, hadronization, and detector effects in detail.
