A Hybrid Scheme for Heavy Flavors: Merging the FFNS and VFNS
A. Kusina, F. I. Olness, I. Schienbein, T. Jezo, K. Kovarik, T. Stavreva, J. Y. Yu
TL;DR
The work introduces the Hybrid Variable Flavor Number Scheme (H-VFNS), which embeds explicit N_F dependence into both PDFs and α_s to generate coexisting NF-specific sets that are linked by MSbar matching. This framework offers maximal flexibility to select the optimal NF for each observable, allowing low-scale FFNS-style fits (e.g., F2^charm at HERA) alongside high-scale VFNS analyses (e.g., LHC processes) without backward evolution. By enabling separate matching and switching scales and retaining multiple NF grids, H-VFNS achieves resummed heavy-quark logs where needed while preserving threshold accuracy, improving the description of heavy-flavor data across kinematics. The approach is demonstrated conceptually with an example involving HERA and LHC data and is supported by detailed discussion of NF-dependent PDFs, α_s running, and their interplay in physical observables.
Abstract
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.
