Implications of Hadron Collider Observables on Parton Distribution Function Uncertainties
Walter T. Giele, Stephane Keller
TL;DR
This paper addresses the lack of quantified uncertainties in parton distribution functions (PDFs) and their impact on hadron-collider interpretations. It introduces a Bayesian inference framework that propagates PDF uncertainties to new observables via Monte Carlo sampling, tests the compatibility of new data with the existing fit using a confidence level, and updates the PDF distributions without redoing full fits. The authors demonstrate the method on two Tevatron datasets (one-jet inclusive cross section and W lepton charge asymmetry), showing that compatible data can tighten PDF uncertainties and shift key parameters, such as $\alpha_S(M_Z)$ and the gluon high-$x$ exponent $β$. The approach is modular and flexible, accommodating experimental and theoretical uncertainties and enabling inclusion or exclusion of data sets as needed, which is crucial for robust SM tests and future LHC analyses.
Abstract
Standard parton distribution function sets do not have rigorously quantified uncertainties. In recent years it has become apparent that these uncertainties play an important role in the interpretation of hadron collider data. In this paper, using the framework of statistical inference, we illustrate a technique that can be used to efficiently propagate the uncertainties to new observables, assess the compatibility of new data with an initial fit, and, in case the compatibility is good, include the new data in the fit.
