重口味的混合方案：合并FFNS和VFNS
A Hybrid Scheme for Heavy Flavors: Merging the FFNS and VFNS
摘要：
我们介绍了一种混合可变味数计划重口味，表示H-VFNS，它结合了传统的可变味数计划（VFNS）以及固定味数计划（FFNS）的优点。通过包括一个明确的$N_F$依赖的部分子分布函数（PDF）和强耦合常数$α_S$，我们生成共存的集合$N_F=\{3,4,5,6\}$在任何规模$μ$，这是相关的分析$\overline{\text{MS}}$匹配条件。H-VFNS对重夸克贡献进行求和，并为每个特定数据集提供选择最佳$N_F$的自由。因此，我们可以在FFNS框架中拟合选定的HERA数据，同时保留VFNS在高尺度下分析LHC数据的优势。我们说明了如何可以实现这种适合的情况下，HERA和大型强子对撞机的数据。
英文摘要：
We introduce a Hybrid Variable Flavor Number Scheme for heavy flavors, denoted H-VFNS, which incorporates the advantages of both the traditional Variable Flavor Number Scheme (VFNS) as well as the Fixed Flavor Number Scheme (FFNS). By including an explicit $N_F$-dependence in both the Parton Distribution Functions (PDFs) and the strong coupling constant $α_S$, we generate coexisting sets of PDFs and $α_S$ for $N_F=\{3,4,5,6\}$ at any scale $μ$, that are related analytically by the $\overline{\text{MS}}$ matching conditions. The H-VFNS resums the heavy quark contributions and provides the freedom to choose the optimal $N_F$ for each particular data set. Thus, we can fit selected HERA data in a FFNS framework, while retaining the benefits of the VFNS to analyze LHC data at high scales. We illustrate how such a fit can be implemented for the case of both HERA and LHC data.
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