A generalized statistical model for fits to parton distributions

TL;DR

This paper introduces a novel Bayesian hierarchical Gaussian mixture model to better estimate uncertainties in parton distribution functions, especially when data sets are inconsistent, improving upon traditional methods.

Contribution

It proposes a generalized statistical model using GMMs inspired by machine learning to accurately capture PDF uncertainties amid data tensions.

Findings

GMM accurately reconstructs likelihoods in toy PDF models

Method reduces to chi-squared likelihood for consistent data

Provides measures to optimize the number of Gaussians

Abstract

Parton distribution functions (PDFs) form an essential part of particle physics calculations. Currently, the most precise predictions for these non-perturbative functions are generated through fits to global data. A problem that several PDF fitting groups encounter is the presence of tension in data sets that appear to pull the fits in different directions. In other words, the best fit depends on the choice of data set. 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 data sets. We further show how this statistical model reduces to the usual chi-squared likelihood function for a consistent data set and provide measures to optimize the number of Gaussians in the GMM.