Description

The understanding of the Quantum Chromo-Dynamics (QCD) is a major challenge of nuclear physics. How the mass of the nucleon emerges from essentially massless constituents ? How the spin of the nucleon build-up from an assembly of quarks and gluons ? These are basic and still unanswered questions. An approach to solve these problems consists in the experimental determination of the distribution of the nucleon constituents (the partons) in the momentum and configuration spaces. These so-called Generalized Parton Distributions (GPDs) encode the correlations between the partons inside the nucleon, and consequently contain information about the structure and dynamics of the nucleon which can ultimately be confronted to lattice QCD calculations.

This project consists in the experimental study of the distribution Eq which is accessed in the Deeply Virtual Compton Scattering (DVCS) reaction off a neutron. It involves the analysis and interpretation of recently acquired experimental data developing and using Machine Learning techniques. It also concerns the development of the next generation of experiments that are the measurements of the Double Deeply Virtual Compton Scattering (DDVCS) reaction in the perspective of the recently approved SoLID(mu) and (mu)CLAS12 experimental projects at the Jefferson Lab (JLab).

Skills required

Confirmed informatic background Confirmed knowledge of physics Artificial Intelligence knowledge would be appreciated

Bibliography

A. Hobart et al. Phys. Rev. Lett. 133 (2024) 211903
J.S. Alvarado, Doctorat de l'Univetrsité Paris-Saclay, 2025
S. Zhao, Doctorat de l'Univetrsité Paris-Saclay, 2020
J.S. Alvarado, M. Hoballah, E. Voutier, Phys. Rev. C 111 (2025) 065205

Keywords

Parton distribution, Deeeply virtual Compton scattering, Double deeply virtual Compton scattering, Artificial intelligence