  We develop a framework to establish benchmarks for machine learning and deep
neural networks analyses of exclusive scattering cross sections (FemtoNet).
Within this framework we present an extraction of Compton form factors for
deeply virtual Compton scattering from an unpolarized proton target. Critical
to this effort is a study of the effects of physics constraint built into
machine learning (ML) algorithms. We use the Bethe-Heitler process, which is
the QED radiative background to deeply virtual Compton scattering, to test our
ML models and, in particular, their ability to generalize information extracted
from data. We then use these techniques on the full cross section and compare
the results to analytic model calculations. We propose a quantification
technique, the random targets method, to begin understanding the separation of
aleatoric and epistemic uncertainties as they are manifest in exclusive
scattering analyses. We propose a set of both physics driven and machine
learning based benchmarks providing a stepping stone towards applying
explainable machine learning techniques with controllable uncertainties in a
wide range of deeply virtual exclusive processes.
