Non-quadratic improved Hessian PDF reweighting and application to CMS dijet measurements at 5.02 TeV
Eskola, K., Paakkinen, P., & Paukkunen, H. (2019). Non-quadratic improved Hessian PDF reweighting and application to CMS dijet measurements at 5.02 TeV. European Physical Journal C, 79(6), Article 511. https://doi.org/10.1140/epjc/s10052-019-6982-2
Published inEuropean Physical Journal C
© The Author(s) 2019.
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 χ2 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 protonPDF 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 ≈ 0.1. ...
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Related funder(s)Academy of Finland
Funding program(s)Research post as Academy Research Fellow, AoF; Academy Project, AoF
Additional information about fundingWe thank Yen-Jie Lee for discussions. We have received funding from the Academy of Finland, Project 297058 of K.J.E. and 308301 of H.P.; P.P. acknowledges the financial support from the Magnus Ehrnrooth Foundation. We thank the Finnish IT Center for Science (CSC) for the computational resources allocated under the Project jyy2580.
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