Separation of Quark Flavors using DVCS Data

Using the available data on deeply virtual Compton scattering (DVCS) off protons and utilizing neural networks enhanced by the dispersion relation constraint, we determine six out of eight leading Compton form factors in the valence quark kinematic region. Furthermore, adding recent data on DVCS off...

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Bibliographic Details
Main Authors Cuic, Marija, Kumericki, Kresimir, Schafer, Andreas
Format Journal Article
LanguageEnglish
Published 30.06.2020
Subjects
Physics - High Energy Physics - Experiment
Physics - High Energy Physics - Phenomenology
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Summary:Using the available data on deeply virtual Compton scattering (DVCS) off protons and utilizing neural networks enhanced by the dispersion relation constraint, we determine six out of eight leading Compton form factors in the valence quark kinematic region. Furthermore, adding recent data on DVCS off neutrons, we separate contributions of up and down quarks to the dominant form factor, thus paving the way towards a three-dimensional picture of the nucleon.
Bibliography:ZTF-EP-20-04
DOI:10.48550/arxiv.2007.00029