18-22 October 2021
Matsue, Shimane Prefecture, Japan
Asia/Tokyo timezone

Artificial neural network techniques in modelling of GPDs

20 Oct 2021, 17:20
Room 501 (Kunibiki Messe)

Room 501

Kunibiki Messe

Parallel Session Presentation Form factors and GPDs Joint GPD - Future session


Paweł Sznajder (National Centre for Nuclear Research)


We discuss the use of machine learning techniques for the modeling of generalized parton distributions in view of their nonparametric estimation from experimental data. Current GPD extractions indeed suffer from a model dependence which lessens their impact and brings unknown systematics in the estimation of derived quantities like 3D tomography or angular momentum decomposition. On the contrary this new strategy to describe GPDs allows a flexible implementation of theory driven constraints and provides tools to keep model dependence at a minimum level. We also address aspects of a practical nature like the design and training of artificial neural networks suitable for this analysis. Getting a better grip on the control of systematic effects, our work will help GPD phenomenology to achieve its maturity in the precision era of GPD extractions opened by a new generation of experiments.

Primary authors

Paweł Sznajder (National Centre for Nuclear Research) Dr Hervé Moutarde (Irfu, CEA) Oskar Grocholski (University of Warsaw)

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