A first determination of parton distributions with theoretical uncertainties

Rabah Abdul Khalek,Cameron Voisey,Tommaso Giani,Michael Wilson,Zahari Kassabov,Stefano Carrazza,Richard D Ball,Stefano Forte,Maria Ubiali,Rosalyn L Pearson,Luca Rottoli,Juan Rojo,Emanuele R Nocera

doi:10.1140/epjc/s10052-019-7364-5

Abstract

The parton distribution functions (PDFs) which characterize the structure of the proton are currently one of the dominant sources of uncertainty in the predictions for most processes measured at the Large Hadron Collider (LHC). Here we present the first extraction of the proton PDFs that accounts for the missing higher order uncertainty (MHOU) in the fixed-order QCD calculations used in PDF determinations. We demonstrate that the MHOU can be included as a contribution to the covariance matrix used for the PDF fit, and then introduce prescriptions for the computation of this covariance matrix using scale variations. We validate our results at next-to-leading order (NLO) by comparison to the known next order (NNLO) corrections. We then construct variants of the NNPDF3.1 NLO PDF set that include the effect of the MHOU, and assess their impact on the central values and uncertainties of the resulting PDFs.

Highlights

parton distribution functions (PDFs) are extracted by comparing theoretical predictions to experimental data
The parton distribution functions (PDFs) which characterize the structure of the proton are currently one of the dominant sources of uncertainty in the predictions for most processes measured at the Large Hadron Collider (LHC)
We present the first extraction of the proton PDFs that accounts for the missing higher order uncertainty (MHOU) in the fixed-order QCD calculations used in PDF determinations

Summary

Introduction

PDFs are extracted by comparing theoretical predictions to experimental data. Currently, PDF uncertainties only account for the propagated statistical and systematic errors on the measurements used in their determination. The projected shifts and eigenvalues are compared in Adding the theory covariance matrix Si j to the experimental covariance matrix Ci j , while increasing the diagonal uncertainty on each individual prediction, (and perhaps more importantly) introduces a set of theory-induced correlations between different experiments and processes, even when the experimental data points are uncorrelated.

Results

Conclusion