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arxiv: 1412.4651 · v1 · pith:NUKMPZOJnew · submitted 2014-12-15 · ✦ hep-ph

Generalized Parton Distributions and Deeply Virtual Compton Scattering

classification ✦ hep-ph
keywords datadistributionsgeneralizedpartonadvantageallowsappliedapproximations
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We present a method which allows to extract theoretical informations out of a limited set of experimental data and observables, forming up in general an under- constrained system. It has been applied to the field of nucleon structure, in the domain of Generalized Parton Distributions (GPDs). We take advantage of this review to remove a couple of approximations that we used in our previous works and update our results using the latest data published.

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    Quantum-inspired deep neural networks extract Compton form factors from JLab data with higher predictive accuracy and tighter uncertainties than classical DNNs on pseudodata benchmarks, then applied to real measurements.