Speaker
Description
The production of a $Z$ boson decaying to muons in association with hadronic jets is a benchmark process in proton--proton collisions at the Large Hadron Collider. It provides a clean experimental signature with high selection efficiency and purity while probing a wide range of Standard Model dynamics. Measurements of this process are typically reported as binned differential cross sections in a limited set of pre-defined observables, which reduces the information retained and hinders reinterpretation. In this talk, the OmniFold unfolding method, which utilizes iteratively trained transformer neural networks, is used to produce an unbinned and highly-differential measurement of the $Z$+jets cross section using 140~fb$^{-1}$ of $\sqrt{s} = 13$ TeV proton--proton collision data collected by the ATLAS detector. This constitutes the first published unbinned, fully differential measurement of a collider cross section in the charged-particle phase space within a fiducial volume. The talk covers details of the application of Omnifold to data, including the novel use of pretrained representations to improve unfolding accuracy and reduce computational cost. Several applications of the measurement to constrain novel jet substructure observables will also be discussed.