Speaker
Description
I will present two complementary analyses of nearly all existing Deeply Virtual Compton Scattering (DVCS) data allowing to explore the proton structure within the framework of Generalised Parton Distributions (GPDs). The first analysis is based on a classic approach, where parameterisations of DVCS amplitudes are constructed in a model-dependent way to fulfil the basic properties of GPDs. The second analysis starts from a model-independent extraction of DVCS amplitudes, which latter are interpreted in the language of GPDs. Both analyses allow to access the nucleon tomography and the so-called subtraction constant, which is related to the energy-momentum tensor and the mechanical forces acting on partons inside the proton. The usage of the neural network technique in the second analysis allows to reduce and estimate the model dependency. The work is done within the PARTONS framework being the modern tool for generic GPD studies.
