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Artificial neural network modelling of generalised parton distributions

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arxiv 2112.10528 v1 pith:V2JVWNUY submitted 2021-12-20 hep-ph

H. Dutrieux , O. Grocholski , H. Moutarde , P. Sznajder This is my paper · ORCID
classification hep-ph
keywords gpdsdependencydistributionsgeneralisedmodelmodellingpartonphenomenology
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We discuss the use of machine learning techniques in effectively nonparametric modelling of generalised parton distributions (GPDs) in view of their future extraction from experimental data. Current parameterisations of GPDs suffer from model dependency that lessens their impact on phenomenology and brings unknown systematics to the estimation of quantities like Mellin moments. The new strategy presented in this study allows to describe GPDs in a way fulfilling theory-driven constraints, keeping model dependency to a minimum. Getting a better grip on the control of systematic effects, our work will help the GPD phenomenology to achieve its maturity in the precision era commenced by the new generation of experiments.

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Cited by 4 Pith papers

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    A review of the EicC physics case argues that the proposed Chinese electron-ion collider, at 15-20 GeV collision energy, can complement the US EIC with high-precision measurements of sea-quark spin structure, proton m...

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