scholarly journals Proton tomography through deeply virtual Compton scattering

2017 ◽  
Vol 4 (2) ◽  
pp. 213-223
Author(s):  
Xiangdong Ji

Abstract In this prize talk, I recall some of the history surrounding the discovery of deeply virtual Compton scattering, and explain why it is an exciting experimental tool to obtain novel tomographic pictures of the nucleons at Jefferson Lab 12 GeV facility and the planned Electron-Ion Collider in the USA. It is certainly a great honor to have received the 2016 Herman Feshbach Prize in theoretical nuclear physics by the American Physical Society. I sincerely thank my colleagues in the Division of Nuclear Physics to recognize the importance of some of the theoretical works I have done in the past, particularly their relevance to the experimental programs around the world.

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

2018 ◽  
Author(s):  
François-Xavier Girod ◽  
Latifa Elouadrhiri ◽  
Volker D. Burkert

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.


2021 ◽  
Vol 57 (7) ◽  
Author(s):  
S. Zhao ◽  
A. Camsonne ◽  
D. Marchand ◽  
M. Mazouz ◽  
N. Sparveris ◽  
...  

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