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arXiv 1106.2808hep-phhep-ex
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神经网络生成的深虚康普顿形状因子参数化

Neural network generated parametrizations of deeply virtual Compton form factors

Kresimir Kumericki, Dieter Mueller, Andreas Schafer

摘要:

我们已经产生了一个参数化的康普顿形状因子(CFF)H的基础上的数据,从深虚拟康普顿散射(DVCS),使用神经网络。这种方法提供了一个基本上与模型无关的拟合过程,它提供了现实的不确定性。此外,它有利于传播的不确定性从实验数据CFFs。我们假设CFF H占主导地位,并使用HERMES数据DVCS关闭非极化质子。我们预测的光束电荷自旋不对称质子的COMPASS II实验的运动学。

英文摘要:

We have generated a parametrization of the Compton form factor (CFF) H based on data from deeply virtual Compton scattering (DVCS) using neural networks. This approach offers an essentially model-independent fitting procedure, which provides realistic uncertainties. Furthermore, it facilitates propagation of uncertainties from experimental data to CFFs. We assumed dominance of the CFF H and used HERMES data on DVCS off unpolarized protons. We predict the beam charge-spin asymmetry for a proton at the kinematics of the COMPASS II experiment.

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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