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We develop a framework to establish benchmarks for machine learning and deepneural networks analyses of exclusive scattering cross sections (FemtoNet).Within this framework we present an extraction of Compton form factors fordeeply virtual Compton scattering from an unpolarized proton target. Criticalto this effort is a study of the effects of physics constraint built intomachine learning (ML) algorithms. We use the Bethe-Heitler process, which isthe QED radiative background to deeply virtual Compton scattering, to test ourML models and, in particular, their ability to generalize information extractedfrom data. We then use these techniques on the full cross section and comparethe results to analytic model calculations. We propose a quantificationtechnique, the random targets method, to begin understanding the separation ofaleatoric and epistemic uncertainties as they are manifest in exclusivescattering analyses. We propose a set of both physics driven and machinelearning based benchmarks providing a stepping stone towards applyingexplainable machine learning techniques with controllable uncertainties in awide range of deeply virtual exclusive processes.

Benchmarks for a Global Extraction of Information from Deeply Virtual Exclusive Scattering