Predictive power of transverse-momentum-dependent distributions
Predictive power of transverse-momentum-dependent distributions
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DOI：
10.1103/physrevd.101.114023
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发表时间：
2020-03
影响因子：
5
通讯作者：
Manvir Grewal;Z. Kang;J. Qiu;A. Signori
中科院分区：
文献类型：
--
作者：
Manvir Grewal;Z. Kang;J. Qiu;A. Signori
我们调查的预测能力的横向动量相关（TMD）分布的光锥动量分数x和硬尺度Q的过程中定义的函数。我们应用鞍点近似的非极化夸克和胶子的横动量分布和评估的鞍点的位置作为运动学的函数。我们定量地确定，非极化横向动量分布的预测能力是最大的大Q和小x区域。对于横截面的预测能力的TMD因式分解形式主义一般通过考虑两个分布的卷积增强，我们明确考虑Z和H0玻色子生产的情况下。在预测能力不是最大的运动学区域，分布对非微扰强子结构敏感。因此，这些地区是至关重要的调查强子层析成像在三维动量空间。
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 H0 boson production. In the kinematic regions where the predictive power is not maximal, the distributions are sensitive to the nonperturbative hadron structure. Thus, these regions are critical for investigating hadron tomography in a three-dimensional momentum space.