论文标题
使用DVCS数据分离夸克口味
Separation of Quark Flavors using DVCS Data
论文作者
论文摘要
使用有关质子的深层虚拟康普顿散射（DVC）的可用数据，并利用了通过分散关系约束增强的神经网络，我们确定价值夸克运动区域中八个领先的康普顿形态中的六个。此外，我们添加了DVC偏离中子的最新数据，我们将夸克和向下夸克的贡献分离为主要的外形因子，从而为核子的三维图片铺平了道路。
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.
