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Generalized statistical model for fits to parton distributions

Mengshi Yan1,*, Tie-Jiun Hou2,†, Zhao Li3,4,‡, Kirtimaan Mohan5,§, and C.-P. Yuan5,∥

  • *Contact author: msyan@pku.edu.cn
  • †Contact author: tjhou@msu.edu
  • ‡Contact author: zhaoli@ihep.ac.cn
  • §Contact author: kamohan@msu.edu
  • ∥Contact author: yuanch@msu.edu
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Phys. Rev. D 112, 034008 – Published 13 August, 2025

DOI: https://doi.org/10.1103/zcsy-34cc

Abstract

Parton distribution functions (PDFs) form an essential part of particle physics calculations. Currently, the most precise predictions for these nonperturbative functions are generated through fits to global data. A problem that several PDF fitting groups encounter is the presence of tension in datasets that appear to pull the fits in different directions. In other words, the best fit depends on the choice of dataset. Several methods to capture the uncertainty in PDFs in presence of seemingly inconsistent fits have been proposed and are currently in use. These methods are important to ensure that uncertainty in PDFs are not underestimated. Here we propose a novel method for estimating the uncertainty by introducing a generalized statistical model based on Bayesian hierarchical models, which is implemented via the Gaussian Mixture Model (GMM). The methodology is inspired by unsupervised machine learning techniques and is closely related to the statistical methods of ensemble learning and Bayesian model averaging. Using a toy model of PDFs, we demonstrate how the GMM can be used to faithfully reconstruct the likelihood associated with PDF fits, which can in turn be used to accurately determine the uncertainty on PDFs, especially in the presence of tension in the fitted datasets. We further show how this statistical model reduces to the usual χ2 likelihood function for a consistent dataset and provide measures to optimize the number of Gaussians in the GMM.

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Outline

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Phys. Rev. D 112, 034008– Published 13 August, 2025
  • Received 1 July 2024
  • Revised 21 May 2025
  • Accepted 21 May 2025
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