Towards a global analysis of generalized parton distributions
Kresimir Kumericki, Dieter Mueller
TL;DR
This paper explores the challenges and methodologies for analyzing generalized parton distributions (GPDs) globally, including model fitting and neural network approaches, highlighting the holographic nature of Radyushkin's ansatz.
Contribution
It introduces neural network fitting techniques for GPDs and discusses the holographic interpretation of Radyushkin's double distribution model.
Findings
Neural networks can effectively fit photon electroproduction data.
Radyushkin's ansatz is interpreted as a holographic GPD model.
Identifies technological needs for comprehensive GPD analysis.
Abstract
We discuss the complexity of GPD phenomenology, comment on the technological needs for a global analysis, and report on model and neural network fits to the photon electroproduction off unpolarized proton. We also point out that Radyushkin's double distribution ansatz is a `holographic' GPD model.
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