Speaker
Description
Deeply virtual exclusive reactions are theorized to be sensitive to the dynamics of bound partons in hadrons through 3D quantum mechanical phase space distributions - the generalized parton distributions; however, there are many steps in the analysis from experimental data to information on hadron structure. The FemtoNet framework was developed to analyze deeply virtual exclusive experimental data using physics-informed deep learning models in order to quantify information loss and reconstruction through the many inverse problems encountered. Simultaneously, the FemtoNet framework leverages a suite of uncertainty quantification techniques to separate epistemic (reducible) and aleatoric (irreducible) errors from the analysis and properly propagate experimental uncertainty. I will demonstrate what physics-informed deep neural networks are capable of in the context of reconstructing lost information from inverse problems in exclusive scattering experiments and give prospects for the future of such a program and consequences for an EIC.
