Protons and neutrons are made of partons (quarks and gluons) that interact via the strong force, governed by Quantum Chromodynamics (QCD). While QCD can be computed at high energies, its complexity reveals itself at low energies, requiring experimental inputs to understand nucleon properties like their mass and spin. The experimental extraction of the Generalized Parton Distributions (GPDs), which describe the correlation of the partons longitudinal momenta and transverse positions within nucleons, provide critical insights into these fundamental properties.
This thesis focuses on analyzing data from the CLAS12 detector, an experiment part of Jefferson Lab's research infrastructure, one the 17 National Laboratory in the USA. CLAS12, a 15-meter-long fixed-target detector with large acceptance, is dedicated to hadronic physics, particularly GPDs extraction. The selected student will study the exclusive photoproduction of the phi meson (gamma p->phi p’), which is sensitive to gluon GPDs, still largely unexplored. The student will develop a framework to study this reaction in the leptonic decay channel (phi -> e+e-) and develop a novel Graph Neural Network-based algorithm to enhance the scattered proton detection efficiency.
The thesis will aim at extracting the cross section of the photoproduction of the phi, and interpret it in term of the proton's internal mass distribution. Hosted at the Laboratory of Nucleon Structure (LSN) at CEA/Irfu in Saclay, this project involves international collaboration within the CLAS collaboration, travel to Jefferson Lab for data collection, and presentations at conferences. Proficiency in particle physics, programming (C++/Python), and English is required. Basic knowledge of particle detectors and Mahine Learning is advantageous but not mandatory.