Impact of heavy quark and quarkonium data on nuclear gluon PDFs

TL;DR

This paper enhances nuclear gluon parton distribution functions (nPDFs) by incorporating extensive LHC quarkonium and heavy-flavor meson data, reducing uncertainties and advancing analysis methods for future collider experiments.

Contribution

It introduces a data-driven approach to include new LHC heavy-flavor data into nPDF fits, improving constraints on the nuclear gluon distribution at very small x values.

Findings

Significantly constrains nuclear gluon PDFs down to x ≈ 10^{-5}

Validates data-driven predictions against NLO NRQCD and GMVFNS calculations

Demonstrates the effectiveness of new methods for future nPDF analyses

Abstract

A clear understanding of nuclear parton distribution functions (nPDFs) plays a crucial role in the interpretation of collider data taken at the Relativistic Heavy Ion Collider (RHIC), the Large Hadron Collider (LHC) and in the near future at the Electron-Ion Collider (EIC). Even with the recent inclusions of vector boson and light meson production data, the uncertainty of the gluon PDF remains substantial and limits the interpretation of heavy ion collision data. To obtain new constraints on the nuclear gluon PDF, we extend our recent nCTEQ15WZ+SIH analysis to inclusive quarkonium and open heavy-flavor meson production data from the LHC. This vast new data set covers a wide kinematic range and puts strong constraints on the nuclear gluon PDF down to $x\lesssim 10^{-5}$. The theoretical predictions for these data sets are obtained from a data-driven approach, where proton-proton data are used to determine effective scattering matrix elements. This approach is validated with detailed comparisons to existing next-to-leading order (NLO) calculations in non-relativistic QCD (NRQCD) for quarkonia and in the general-mass variable-flavor-number scheme (GMVFNS) for the open heavy-flavored mesons. In addition, the uncertainties from the data-driven approach are determined using the Hessian method and accounted for in the PDF fits. This extension of our previous analyses represents an important step toward the next generation of PDFs not only by including new data sets, but also by exploring new methods for future analyses.