论文标题
晶格QCD和机器学习的细胞核中的Gluon螺旋分布
Gluon helicity distribution in the nucleon from lattice QCD and machine learning
论文作者
论文摘要
我们介绍了第一个晶格QCD测定光锥形螺旋相关性parton分布函数（PDF），并具有数值证据，以不利于细胞核中的负gluon偏振。我们提出了消除不可避免的污染项的解决方案，该术语主导了欧几里得相关性，并确定了Gluon Helicity PDF不可行。晶格QCD和人工智能之间提出的协同作用提供了一个卓越的平台，以减轻从晶格数据中提取夸克和gluon PDF的决定性挑战，这些数据是由于有限的范围内可用于有限域而在有限域中可用的可访问的Hadron Momenta。我们建议一种可系统地改进的方法，可以从晶格数据中提取PDF，而与参数化不足有关。 Gluon螺旋度的结果将提高我们对自旋在强相互作用和核子自旋结构中的作用的理解。
We present the first lattice QCD determination of the light cone gluon helicity correlation parton distribution function (PDF) with numerical evidence toward disfavoring negative gluon polarization in the nucleon. We present a solution for eliminating an inevitable contamination term that dominates the Euclidean correlations and makes determining gluon helicity PDF unfeasible. The proposed synergy between lattice QCD and artificial intelligence offers a superior platform to alleviate the defining challenge of extracting quark and gluon PDFs from the lattice data that are available in a limited domain due to a finite range of accessible hadron momenta. We suggest a systematically improvable method to extract PDFs from the lattice data, independent of inadequate parametrizations. The result of the gluon helicity will improve our understanding of the role of spin in the strong interaction and the nucleon spin structure.
