A High- and Variable-Dimensional Measurement of the $Z$+jets Differential Cross Section with the ATLAS Experiment and Artificial Intelligence

Abstract

Proton-proton collisions at the Large Hadron Collider (LHC) offer the opportunity to observe the interactions of fundamental particles at very high energy scales. The high collision rate and large number of final state particles produced per collision imply that the datasets produced by detectors such as the ATLAS experiment are large and high dimensional. The complexity of these datasets calls for the use of novel data analysis techniques which can exploit all of the available information to illuminate known interactions and search for new ones. This thesis presents applications of artificial intelligence (AI) techniques to improve the physics results of the ATLAS experiment. It centers on a full-phase-space measurement of the Z+jets production cross section at the LHC, where the cross section is measured differential in the kinematics of every final state charged particle through the use of an AI-based unfolding algorithm. This is the first such measurement performed at the LHC, which provides a complete experimental characterization of the Z+jets production process and a proof-of-principle for the further pursuit of such measurements on a host of LHC processes.