Speaker
Description
Modern accelerator facilities operating at the intensity frontier—such as CERN, Jefferson Lab, and the forthcoming EIC—produce petabyte-scale datasets that probe the structure of visible matter at the femtometer scale. Fully exploiting and preserving this information requires new AI-driven strategies for data analysis and modeling. We present a program to develop Machine-Learning-based Physics Event Generators (MLEGs) using state-of-the-art Generative Adversarial Networks (GANs) and Diffusion Models trained directly on scattering data.
The goals of this work are threefold: (i) reproduce reaction dynamics through data-driven generative modeling; (ii) unfold detector effects to recover underlying truth-level distributions; and (iii) enable model-independent analyses that yield new insights into the mechanisms of elementary scattering processes. A rigorous uncertainty-quantification framework is incorporated to characterize data, model, and generative uncertainties. This international collaboration between experimental and theoretical physicists, as well as computer scientists, aims to demonstrate how generative AI—specifically, GANs and Diffusion Models—can transform data analysis and long-term data preservation in nuclear and particle physics.
In this contribution, I will present the general framework and its validation in inclusive and semi-inclusive deep-inelastic electron–proton scattering and in exclusive hadron photoproduction.