Simultaneous Unbinned Differential Cross-Section Measurement of Twenty-Four $Z+\text{jets}$ Kinematic Observables with the ATLAS Detector

$Z$ 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 $Z+\text{jets}$ observables using $139\text{}\text{}{\mathrm{fb}}^{-1}$ of proton-proton collisions at $\sqrt{s}=13\text{}\text{}\mathrm{TeV}$ 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 reuse in a variety of contexts and for new observables to be constructed from the twenty-four measured observables.