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Using the helicity amplitudes formalism, we study deeply virtual exclusive electron photoproduction off an unpolarized nucleon target, ep→e′p′γ, through a range of kinematics both in the fixed target setting with initial electron energies of 6, 11, and 24 GeV and for an electron ion collider. We reformulate the cross section bringing to the forefront the defining features of the ep→e′p′γ process, where the observables are expressed as bilinear products of the independent helicity amplitudes which completely describe it in terms of the electric, magnetic, and axial currents of the nucleon. These contributions are checked against the Fourier harmonics-based formalism which has provided so far the underlying mathematical framework to study deeply virtual Compton scattering (DVCS) and related experiments. Using theoretical model calculations of the twist-two generalized parton distributions, H, E, ˜H, and ˜E, we uncover large discrepancies between the harmonic series and our proposed framework. Most importantly, these numerical differences appear in the intermediate Q² range which represents a sweet spot for extracting generalized parton distributions from data. We provide a framework that is ideal, on one side, to study and compare the different conventions that can be used to describe the leading order contribution to DVCS in QCD, while on the other it facilitates a quantitative extraction of physically meaningful information from experiment through traceable and controllable approximations in the intermediate Q² region. [ABSTRACT FROM AUTHOR]

We present a machine learning based approach to the cross section and asymmetries for deeply virtual Compton scattering from an unpolarized proton target using both an unpolarized and polarized electron beam. Machine learning methods are needed to study and eventually interpret the outcome of deeply virtual exclusive experiments since these reactions are characterized by a complex final state with a larger number of kinematic variables and observables, exponentially increasing the difficulty of quantitative analyses. Our deep neural network (FemtoNet) uncovers emergent features in the data and learns an accurate approximation of the cross section that outperforms standard baselines. FemtoNet reveals that the predictions in the unpolarized case systematically show a smaller relative median error than the polarized that can be ascribed to the presence of the Bethe-Heitler process. It also suggests that the t dependence can be more easily extrapolated than for the other variables, namely the skewness, ξ and four-momentum transfer, Q². Our approach is fully scalable and will be capable of handling larger datasets as they are released from future experiments. [ABSTRACT FROM AUTHOR]