60 citations · 243 across the 18 of their papers we have counts for
4 papers · 1 filter
Parametrizing Compton form factors with neural networks
Kresimir Kumericki, Dieter Mueller, Andreas Schafer
We describe a method, based on neural networks, of revealing Compton form factors in the deeply virtual region. We compare this approach to standard least-squares model fitting bot…
Studying Deeply Virtual Compton Scattering with Neural Networks
Kresimir Kumericki, Dieter Mueller, Andreas Schafer
Neural networks are utilized to fit Compton form factor H to HERMES data on deeply virtual Compton scattering off unpolarized protons. We used this result to predict the beam charg…
GPD and PDF modeling in terms of effective light-cone wave functions
Dae Sung Hwang, Dieter Mueller
We employ models from effective two-body light-cone wave functions (LCWFs) to provide a link between generalized parton distributions (GPDs) and unintegrated parton distribution fu…
Accessing GPDs from experiment --- potential of a high-luminosity EIC ---
Kresimir Kumericki, Tobias Lautenschlager, Dieter Mueller +3
We discuss modeling of generalized parton distributions (GPDs), their access from present experiments, and the phenomenological potential of an electron-ion collider. In particular…