论文标题
横向摩托明依赖性分布的预测能力
Predictive power of transverse-momentum-dependent distributions
论文作者
论文摘要
我们研究了横向动量分数$ x $和该过程定义的硬尺度$ q $的函数，研究了横向摩托明依赖性（TMD）分布的预测能力。我们将鞍点近似应用于非极化夸克和Gluon横向动量分布，并评估鞍点的位置随运动学的函数。我们定量地确定，在大$ q $和小$ x $区域中，不偏振横向动量分布的预测能力是最大的。对于横截面，通常通过考虑两个分布的卷积来增强TMD分解形式主义的预测能力，我们明确考虑$ Z $和$ h^0 $ boson生产的情况。在预测能力不是最大的运动区域中，分布对非扰动强子结构敏感。因此，这些区域对于在三维动量空间中研究强子断层扫描至关重要。
We investigate the predictive power of transverse-momentum-dependent (TMD) distributions as a function of the light-cone momentum fraction $x$ and the hard scale $Q$ defined by the process. We apply the saddle point approximation to the unpolarized quark and gluon transverse momentum distributions and evaluate the position of the saddle point as a function of the kinematics. We determine quantitatively that the predictive power for an unpolarized transverse momentum distribution is maximal in the large-$Q$ and small-$x$ region. For cross sections the predictive power of the TMD factorization formalism is generally enhanced by considering the convolution of two distributions, and we explicitly consider the case of $Z$ and $H^0$ boson production. In the kinematic regions where the predictive power is not maximal, the distributions are sensitive to the non-perturbative hadron structure. Thus, these regions are critical for investigating hadron tomography in a three-dimensional momentum space.
