English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Tools annual

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Tools monthly

Global: Zendy Plus monthly

Presents the access of premium content as premium feature

Premium Content

Global: Zendy Plus annual

Global: Zendy Free Plan

Relying on the polynomiality property of generalized parton distributions,which roots on Lorentz covariance, we prove that it is enough to know them atvanishing- and low-skewness within the DGLAP region to obtain a uniqueextension to their entire support up to a D-term. We put this idea in practiceusing two methods: Reconstruction using artificial neural networks andfinite-elements methods. We benchmark our results against standard models forgeneralized parton distributions. In agreement with the formal expectation, weobtain a very accurate reconstructions for a maximal value of the skewness aslow as 20% of the longitudinal momentum fraction. This result might be relevantfor reconstruction of generalized parton distribution from experimental andlattice QCD data, where computations are for now, restricted in skewness.

Unraveling Generalized Parton Distributions Through Lorentz Symmetry and Partial DGLAP Knowledge