论文标题
重夸克和夸克数据对核gluon PDF的影响
Impact of heavy quark and quarkonium data on nuclear gluon PDFs
论文作者
论文摘要
对核部分分布函数(NPDF)的清晰了解在解释相对论重离子对撞机(RHIC),大型强子对撞机(LHC)的对撞机数据中起着至关重要的作用。即使是最近的载体玻色子和光中膜生产数据的包含,Gluon PDF的不确定性仍然很大,并限制了重离子碰撞数据的解释。为了获得对核Gluon PDF的新约束,我们将最近的NCTEQ15WZ+SIH分析扩展到了LHC的包含Quarkonium和开放的重型梅森生产数据。这个庞大的新数据集涵盖了广泛的运动范围,并将核Gluon PDF的强大限制降低到$ x \ Lessim 10^{ - 5} $。这些数据集的理论预测是从数据驱动的方法获得的,该方法使用质子 - 质子数据来确定有效的散射矩阵元素。通过详细的比较与Quarkonia的非相关性QCD(NRQCD)以及开放的重型味米松的一般质量可变味 - 味 - 名称(GMVFN)中的现有近代领先顺序(NRQCD)中的现有近代序列(NLO)计算进行了验证。此外,使用Hessian方法确定数据驱动方法的不确定性,并在PDF拟合中进行解释。我们以前的分析的这种扩展是迈向下一代PDF的重要步骤,不仅是包括新数据集,而且还通过探索未来分析的新方法。
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.