Machine learning methods for extracting structure functions from experimental data

Machine learning methods are used to extract structure functions from experimental deep-inelastic scattering cross section data with uncertainty predictions, without the need for explicit parameterizations for the structure functions. Structure functions are predicted without direct supervision, with learning models trained using explicit supervision on cross sections directly. Results from generative adversarial networks and deep regression models will be presented, and predictions with uncertainties from different methods compared.