scholarly journals Phenomenological assessment of proton mechanical properties from deeply virtual Compton scattering

2021 ◽  
Vol 81 (4) ◽  
Author(s):  
H. Dutrieux ◽  
C. Lorcé ◽  
H. Moutarde ◽  
P. Sznajder ◽  
A. Trawiński ◽  
...  

AbstractA unique feature of generalised parton distributions is their relation to the QCD energy–momentum tensor. In particular, they provide access to the mechanical properties of the proton i.e. the distributions of pressure and shear stress induced by its quark and gluon structure. In principle the pressure distribution can be experimentally determined in a model-independent way from a dispersive analysis of deeply virtual Compton scattering data through the measurement of the subtraction constant. In practice the kinematic coverage and accuracy of existing experimental data make this endeavour a challenge. Elaborating on recent global fits of deeply virtual Compton scattering measurements using artificial neural networks, our analysis presents the current knowledge on this subtraction constant and assesses the impact of the most frequent systematic assumptions made in this field of research. This study will pave the way for future works when more precise data will become available, e.g. obtained in the foreseen electron-ion colliders EIC and EIcC.

2009 ◽  
Vol 24 (35n37) ◽  
pp. 2838-2847 ◽  
Author(s):  
DIETER MÜLLER ◽  
KREŠIMIR KUMERIČKI

We analyze small-x deeply virtual Compton scattering data using flexible generalized parton distribution models to pin down both the skewness and t dependence. We compare our outcome at t = 0 with the full Shuvaev transformation. We point out that this integral transform is a model which is equivalent to a conformal generalized parton distribution and a minimalist "dual" parameterization. Some mathematical subtleties of conformal representations are recalled.


2015 ◽  
Vol 37 ◽  
pp. 1560042 ◽  
Author(s):  
K. Kumerički ◽  
D. Mueller

Several approaches to extraction of Generalized Parton Distributions (GPDs) from Deeply Virtual Compton Scattering (DVCS) data are presented. In particular, local model-independent fits are compared to neural network approach.


2021 ◽  
Vol 103 (11) ◽  
Author(s):  
V. Bertone ◽  
H. Dutrieux ◽  
C. Mezrag ◽  
H. Moutarde ◽  
P. Sznajder

2020 ◽  
Vol 102 (6) ◽  
Author(s):  
Sara Fucini ◽  
Sergio Scopetta ◽  
Michele Viviani

2000 ◽  
Vol 666-667 ◽  
pp. 234-243
Author(s):  
P.A.M. Guichon ◽  
M. Guidal ◽  
M. Vanderhaeghen

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