利用深度非弹性康普顿散射数据分离夸克味

Separation of Quark Flavors Using Deeply Virtual Compton Scattering Data.

作者信息

Čuić Marija, Kumerički Krešimir, Schäfer Andreas

机构信息

Department of Physics, Faculty of Science, University of Zagreb, 10000 Zagreb, Croatia.

Institüt für Theoretische Physik, Universität Regensburg, D-93040 Regensburg, Germany.

出版信息

Phys Rev Lett. 2020 Dec 4;125(23):232005. doi: 10.1103/PhysRevLett.125.232005.

DOI:10.1103/PhysRevLett.125.232005

PMID:33337223

Abstract

Using the available data on deeply virtual Compton scattering (DVCS) off protons and utilizing neural networks enhanced by the dispersion relation constraint, we determine six out of eight leading Compton form factors in the valence quark kinematic region. Furthermore, adding recent data on DVCS off neutrons, we separate contributions of up and down quarks to the dominant form factor, thus paving the way towards a three-dimensional picture of the nucleon.

摘要

利用质子上深度虚康普顿散射（DVCS）的现有数据，并借助由色散关系约束增强的神经网络，我们在价夸克运动学区域确定了八个主要康普顿形状因子中的六个。此外，加上最近关于中子上DVCS的数据，我们分离了上夸克和下夸克对主导形状因子的贡献，从而为构建核子的三维图像铺平了道路。