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arXiv 1303.7236hep-ph
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极化部分子分布的无偏确定及其不确定性

Unbiased determination of polarized parton distributions and their uncertainties

The NNPDF Collaboration, Richard D. Ball, Stefano Forte, Alberto Guffanti, Emanuele R. Nocera, Giovanni Ridolfi, Juan Rojo

摘要:

我们提出了一个确定的一组极化部分子分布(PDF)的核子,在下一个领先的顺序,从一组全球性的纵向极化深非弹性散射数据:NNPDFpol1.0。该决定是基于NNPDF方法:蒙特卡罗方法,神经网络作为无偏插值,以前应用于非极化部分子分布的测定,并设计提供一个忠实的和统计上合理的PDF不确定性表示。我们介绍了我们的数据集,它的统计特征,以及它的蒙特卡罗表示。我们总结了用于解决极化演化方程的技术和它的基准测试,以及用于计算物理可观测量的方法。我们回顾了NNPDF方法的参数化和拟合的神经网络,用于确定最佳拟合的算法,其适应极化的情况下。最后,我们提出了我们的极化部分子分布。我们讨论了它的统计特性,测试其稳定性后的各种修改的拟合过程,并将其与其他最近的极化部分子集,特别是获得预测极化的第一时刻的PDF的基础上it.We发现,胶子的不确定性,并在较小程度上奇怪的PDF,大大低估了在以前的测定。

英文摘要:

We present a determination of a set of polarized parton distributions (PDFs) of the nucleon, at next-to-leading order, from a global set of longitudinally polarized deep-inelastic scattering data: NNPDFpol1.0. The determination is based on the NNPDF methodology: a Monte Carlo approach, with neural networks used as unbiased interpolants, previously applied to the determination of unpolarized parton distributions, and designed to provide a faithful and statistically sound representation of PDF uncertainties. We present our dataset, its statistical features, and its Monte Carlo representation. We summarize the technique used to solve the polarized evolution equations and its benchmarking, and the method used to compute physical observables. We review the NNPDF methodology for parametrization and fitting of neural networks, the algorithm used to determine the optimal fit, and its adaptation to the polarized case. We finally present our set of polarized parton distributions. We discuss its statistical properties, test for its stability upon various modifications of the fitting procedure, and compare it to other recent polarized parton sets, and in particular obtain predictions for polarized first moments of PDFs based on it. We find that the uncertainties on the gluon, and to a lesser extent the strange PDF, were substantially underestimated in previous determinations.

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