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We report on a new framework to parametrize parton distribution functions(PDFs) and other hadronic nonperturbative functions using polynomial functionsrealized by B\'ezier curves. B\'ezier parameterizations produce a stable fitwith a low number of free parameters, while competing in performance withneural networks and offering explicit interpretation. We specifically applythis approach to determine PDFs in a pion, essential for understanding ofnonperturbative QCD dynamics.

Fant\^omas For QCD: parton distributions in a pion with B\'ezier parametrizations