Bjorken求和规则的Monte Carlo和神经网络方法
The Bjorken sum rule with Monte Carlo and Neural Network techniques
摘要：
结构函数和部分子分布函数的确定最近已经得到使用Monte Carlo方法和神经网络作为通用的，无偏的插值未知的功能依赖。在这项工作中，同样的方法被应用到获得极化深度非弹性散射（DIS）结构函数的参数化。蒙特卡罗方法提供了一个无偏的确定的概率测度的空间结构函数，同时保留所有的信息实验误差和相关性。特别地，数据上的误差被传播为具有明确统计意义的结构函数上的误差。我们提出了应用这种方法的参数化从偏振DIS数据的光子不对称性$A_1^p$和$A_1^d$，我们确定的结构函数$g_1^p(x,Q^2)$和$g_1^d(x,Q^2)$，并讨论了可能性提取的物理参数从这些参数化。这项工作可以作为一个出发点的极化部分子分布的测定。
英文摘要：
Determinations of structure functions and parton distribution functions have been recently obtained using Monte Carlo methods and neural networks as universal, unbiased interpolants for the unknown functional dependence. In this work the same methods are applied to obtain a parametrization of polarized Deep Inelastic Scattering (DIS) structure functions. The Monte Carlo approach provides a bias--free determination of the probability measure in the space of structure functions, while retaining all the information on experimental errors and correlations. In particular the error on the data is propagated into an error on the structure functions that has a clear statistical meaning. We present the application of this method to the parametrization from polarized DIS data of the photon asymmetries $A_1^p$ and $A_1^d$ from which we determine the structure functions $g_1^p(x,Q^2)$ and $g_1^d(x,Q^2)$, and discuss the possibility to extract physical parameters from these parametrizations. This work can be used as a starting point for the determination of polarized parton distributions.
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