Unraveling Quantum Chromodynamics

At energy-frontier facilities such as the Large Hadron Collider (LHC), scientists study the laws of nature in their quest for novel phenomena both within and beyond the Standard Model of particle physics. An in-depth understanding of the quark and gluon substructure of protons and heavy nuclei is crucial to address pressing questions from the nature of the Higgs boson to the origin of cosmic neutrinos. The key to address some of these questions is carrying out a global analysis of nucleon structure by combining an extensive experimental dataset and cutting-edge theory calculations. Within the NNPDF approach, this is achieved by means of a machine learning framework where neural networks parametrise the underlying physical laws while minimising ad-hoc model assumptions. In addition to the LHC, the upcoming Electron Ion Collider (EIC), to start taking data in 2030+, will be the world’s first ever polarised lepton-hadron collider and will offer a plethora of opportunities to address key open questions in our understanding of the strong nuclear force, such as the origin of the mass and the intrinsic angular momentum (spin) of hadrons and whether there exists a state of matter which is entirely dominated by gluons. In this project, the student will develop novel machine learning and AI approaches aimed to improve global analyses of proton structure and fragmentation functions and to provide better predictions for the LHC, the EIC, and astroparticle physics experiments. These new approaches will be implemented within the machine learning tools provided by the NNPDF open-source fitting framework and use state-of-the-art calculations in perturbative Quantum Chromodynamics. In particular, we aim to carry out a first determination of identified hadron fragmentation functions at next-to-next-to-leading order accuracy, and a first integrated global QCD analysis of polarised and unpolarised parton distributions together with the hadron fragmentation functions.