We apply a classical mathematical problem, the moment problem, with its related mathematical achievements, to the study of the parton distribution function (PDF) in hadron physics, and propose a strategy to sieve the moments of the PDF by exploiting its properties such as continuity, unimodality, and symmetry. Through an error-inclusive sifting process, we refine three sets of PDF moments from Lattice QCD. This refinement significantly reduces the errors, particularly for higher order moments, and locates the peak of PDF simultaneously. As our strategy is universally applicable to PDF moments from any method, we strongly advocate its integration into all PDF moment calculations.
@article{arxiv.2308.14871,
title = {Sieving parton distribution function moments via the moment problem},
author = {Xiaobin Wang and Minghui Ding and Lei Chang},
journal= {arXiv preprint arXiv:2308.14871},
year = {2023}
}