  AI-supported algorithms, particularly generative models, have been
successfully used in a variety of different contexts. In this work, we
demonstrate for the first time that generative adversarial networks (GANs) can
be used in high-energy experimental physics to unfold detector effects from
multi-particle final states, while preserving correlations between kinematic
variables in multidimensional phase space. We perform a full closure test on
two-pion photoproduction pseudodata generated with a realistic model in the
kinematics of the Jefferson Lab CLAS g11 experiment. The overlap of different
reaction mechanisms leading to the same final state associated with the CLAS
detector's nontrivial effects represents an ideal test case for AI-supported
analysis. Uncertainty quantification performed via bootstrap provides an
estimate of the systematic uncertainty associated with the procedure. The test
demonstrates that GANs can reproduce highly correlated multidifferential cross
sections even in the presence of detector-induced distortions in the training
datasets, and provides a solid basis for applying the framework to real
experimental data.
