Mathematical inverse problem for the world data inference of the parton distribution functions of the proton

Abstract: We show that the inference problem of constraining the parton distribution functions of the proton from deeply inelastic scattering data can be formulated as a linear tensor reconstruction inverse problem. This means that instead of fitting model parameters to the world data, a reconstructive approach to solve a system of coupled linear functional integral equations can be formulated. We leverage the mathematical structures of the integral equations defined by perturbative QCD and global analysis to pose the problem of global analysis as a coupled system of linear integral equations, and construct a proof-of-principle methodology to solve it. To formulate this approach concretely, we review all next-to-leading order accuracy results for inclusive virtual photon, neutral current, charged current, heavy flavor production, and neutrino deep-inelastic lepton--proton scattering. This mathematical methodology of inference opens a path towards a model-bias-free extraction of the proton PDFs from the world data of deeply inelastic scattering in the spirit of indirect measurement employed in mathematical inverse problems, including robust estimation of uncertainties with reduced bias from model parametrization, while closely adhering to the established paradigm of perturbative QCD and global analysis.