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AI-supported algorithms, particularly generative models, have beensuccessfully used in a variety of different contexts. In this work, wedemonstrate for the first time that generative adversarial networks (GANs) canbe used in high-energy experimental physics to unfold detector effects frommulti-particle final states, while preserving correlations between kinematicvariables in multidimensional phase space. We perform a full closure test ontwo-pion photoproduction pseudodata generated with a realistic model in thekinematics of the Jefferson Lab CLAS g11 experiment. The overlap of differentreaction mechanisms leading to the same final state associated with the CLASdetector's nontrivial effects represents an ideal test case for AI-supportedanalysis. Uncertainty quantification performed via bootstrap provides anestimate of the systematic uncertainty associated with the procedure. The testdemonstrates that GANs can reproduce highly correlated multidifferential crosssections even in the presence of detector-induced distortions in the trainingdatasets, and provides a solid basis for applying the framework to realexperimental data.

Toward a generative modeling analysis of CLAS exclusive $2\pi$ photoproduction