Non-quadratic improved Hessian PDF reweighting and application to CMS dijet measurements at 5.02 TeV
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
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.
Main Authors
Format
Articles
Research article
Published
2019
Series
Subjects
Publication in research information system
Publisher
Springer
The permanent address of the publication
https://urn.fi/URN:NBN:fi:jyu-201906203358Use this for linking
Review status
Peer reviewed
ISSN
1434-6044
DOI
https://doi.org/10.1140/epjc/s10052-019-6982-2
Language
English
Published in
European Physical Journal C
Citation
- 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
Funder(s)
Research Council of Finland
Research Council of Finland
Funding program(s)
Akatemiatutkija, SA
Akatemiahanke, SA
Academy Research Fellow, AoF
Academy Project, AoF

Additional information about funding
We 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.
Copyright© The Author(s) 2019.