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arXiv 2007.00029hep-phhep-ex
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用DVCS数据分离夸克味子

Separation of Quark Flavors using DVCS Data

Marija Cuic, Kresimir Kumericki, Andreas Schafer

摘要:

利用已有的质子深虚康普顿散射(DVCS)数据,利用色散关系约束增强的神经网络,确定了价夸克运动学区域八个主要康普顿形状因子中的六个.此外,添加最近的数据DVCS关闭中子,我们分开的贡献,上夸克和下夸克占主导地位的形状因子,从而铺平了道路的三维图片的核子。

英文摘要:

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.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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