  $Z$ boson events at the Large Hadron Collider can be selected with high
purity and are sensitive to a diverse range of QCD phenomena. As a result,
these events are often used to probe the nature of the strong force, improve
Monte Carlo event generators, and search for deviations from Standard Model
predictions. All previous measurements of $Z$ boson production characterize the
event properties using a small number of observables and present the results as
differential cross sections in predetermined bins. In this analysis, a machine
learning method called OmniFold is used to produce a simultaneous measurement
of twenty-four $Z$+jets observables using $139$ fb$^{-1}$ of proton-proton
collisions at $\sqrt{s}=13$ TeV collected with the ATLAS detector. Unlike any
previous fiducial differential cross-section measurement, this result is
presented unbinned as a dataset of particle-level events, allowing for flexible
re-use in a variety of contexts and for new observables to be constructed from
the twenty-four measured observables.
