首页> 外文期刊>Physical review, C >QCD analysis of nucleon structure functions in deep-inelastic neutrino-nucleon scattering: Laplace transform and Jacobi polynomials approach
【24h】

QCD analysis of nucleon structure functions in deep-inelastic neutrino-nucleon scattering: Laplace transform and Jacobi polynomials approach

机译:深核 - 核心核散射中核结构核心核心分析的QCD分析:拉普拉斯变换与雅各比多项式方法

获取原文
获取原文并翻译 | 示例
           

摘要

We present a detailed QCD analysis of nucleon structure functions xF(3)(x,Q(2)), based on Laplace transforms and the Jacobi polynomials approach. The analysis corresponds to the next-to-leading order and next-to-next-to-leading order approximations of perturbative QCD. The Laplace transform technique, as an exact analytical solution, is used for the solution of nonsinglet Dokshitzer-Gribov-Lipatov-Altarelli-Parisi evolution equations at low- and large-x values. The extracted results are used as input to obtain the x and Q(2) evolution of xF(3)(x,Q(2)) structure functions using the Jacobi polynomials approach. In our work, the values of the typical QCD scale Lambda(n(f))/MS and the strong coupling constant as alpha(s)(M-Z(2)) are determined for four quark flavors (n(f) = 4) as well. A careful estimation of the uncertainties shall be performed using the Hessian method for the valence-quark distributions, originating from the experimental errors. We compare our valence-quark parton distribution functions sets with those of other collaborations, in particular with the CT14, MMHT14, and NNPDF sets, which are contemporary with the present analysis. The obtained results from the analysis are in good agreement with those from the literature.
机译:我们介绍了核心结构函数XF(3)(X,Q(2))的详细QCD分析,基于拉普拉斯变换和雅各比多项式方法。分析对应于扰动QCD的下一端订单和下一对一的术语近似。 Laplace变换技术作为精确的分析解决方案,用于低于和大X值的非通道Dokshitzer-Gribatov-Altarelli-Parisi进化方程的解决方案。提取的结果用作输入以获得XF(3)(X,Q(2))结构函数的X和Q(2)的演变,该XF(x,q(2))结构函数的结构函数。在我们的工作中,确定四种夸克口味(N(f)= 4)确定典型QCD尺度λ(n(f))/ ms和强耦合常数强的耦合常数(mz(2))也是。仔细估计不确定性,应使用Hessian方法进行价值 - 夸克分布,来自实验误差。我们将Venets-Quark Parton分发功能与其他合作,特别是与当代分析的CT14,MMHT14和NNPDF集进行比较。分析的获得结果与文献中的人吻合良好。

著录项

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

客服邮箱:kefu@zhangqiaokeyan.com

京公网安备:11010802029741号 ICP备案号:京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有
  • 客服微信

  • 服务号