CNF Research

Imaging the proton through generalized parton distributions

Generalized parton distributions (GPDs) provide an important theoretical tool to visualize the proton. In the early days, physicists study the internal structure through form factors (or structure factors), which can be measured through diffractive scattering of the electrons. However, this only provides the static information without understanding of the dynamics. A dynamical information is provided by the momentum distributions of constituents, which give us the snapshots of the proton in momentum space. GPDs are a type of “phase-space” distributions which interpolate between the form factors and momentum distributions, providing the simultaneous pictures of where the constituents are and how much momentum they carry.

High-energy probes of the nuclear femto-structure

A new type of nuclear structure probe is called deeply virtual exclusive processes (DVEPs). One important example of DVEPs is called deeply virtual Compton scattering (DVCS), in which a highly virtual photon scatters on a nuclear system (proton, neutron etc),which then recoils after radiating a high-energy photon. When the real photon is replaced by mesons, we have other DVEPs. In DVEPs, the quark and gluon degrees of freedom in the target are directly responsible for the scattering, which in turn allow for probes of the quark and gluon dynamics in nuclear systems. The recently upgraded 12 GeV Continuous Electron Beam Accelerator Facility at Jefferson Lab and the future Electron-Ion Collider will study the proton and other nuclear systems through this powerful new process.

Large-scale QCD simulations of the proton structure

Quantum Chromodynamics (QCD) is the fundamental theory of strong interactions. It is a field theory that can be approximated on a four-dimensional Euclidean lattice, allowing researchers to use supercomputers to calculate this theory in lattice QCD. Although many properties of the proton, such as mass and charge distribution can be readily calculated in lattice QCD, its structure as probed by high-energy electron beams cannot be computed on the lattice in a straightforward way. It must be approximated through an additional theoretical formalism, called large-momentum effective theory.

Machine Learning/Artificial Intelligence in Femtography

In Nuclear Femtography, many intermediate steps involve large amounts of data. Using ML/AI techniques in those steps will allow nuclear physicists to obtain results in a timely and resource-saving manner. From simulations of deeply virtual Compton scattering events, to the storage and retrieval of experimental data, to solving the inverse problem of extracting the generalized parton distributions from experiments, to large-scale lattice quantum chromodynamics simulations, ML/AI will provide much needed help.