Abstract
Hessian PDF reweighting, or “profiling”, has become a widely used way to study the impact of a new data set on parton distribution functions (PDFs) with Hessian error sets. The available implementations of this method have resorted to a perfectly quadratic approximation of the initial chi ^2 function before inclusion of the new data. We demonstrate how one can take into account the first non-quadratic components of the original fit in the reweighting, provided that the necessary information is available. We then apply this method to the CMS measurement of dijet pseudorapidity spectra in proton–proton (pp) and proton–lead (pPb) collisions at 5.02 TeV. The measured pp dijet spectra disagree with next-to-leading order (NLO) theory calculations using the CT14 NLO PDFs, but upon reweighting the CT14 PDFs, these can be brought to a much better agreement. We show that the needed proton-PDF modifications also have a significant impact on the predictions for the pPb dijet distributions. Taking the ratio of the individual spectra, the proton-PDF uncertainties effectively cancel, giving a clean probe of the PDF nuclear modifications. We show that these data can be used to further constrain the EPPS16 nuclear PDFs and strongly support gluon nuclear shadowing at small x and antishadowing at around x approx 0.1.
Highlights
As producing a full global fit remains rather involved, even with publicly available tools like the xFitter [7] coming available, it is in most cases impractical for a general user to try to learn about the constraining power of a data set in this way
We show that the strong disagreement between the pp measurement and next-to-leading order (NLO) calculations using CT14 NLO parton distribution functions (PDFs) [5] can be brought to a much better agreement upon reweighting the CT14 PDFs, but that this requires rather strong modifications for high-x gluons
We have presented a non-quadratic extension of the Hessian PDF reweighting introduced in Ref. [15] and applied the method in the context of CMS dijet measurements at 5.02 TeV
Summary
As producing a full global fit remains rather involved, even with publicly available tools like the xFitter [7] (built upon the former HERAFitter [8]) coming available, it is in most cases impractical for a general user to try to learn about the constraining power of a data set in this way For this purpose, approximative methods have been developed, first in the formalism of Bayesian reweighting of Monte Carlo PDF ensembles [9,10,11,12,13] and later in a framework using Hessian error sets [14,15,16]. Preliminary work on this topic can be found in Refs. [19,20]
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