论文标题
EKO：进化内核操作员
EKO: Evolution Kernel Operators
论文作者
论文摘要
我们提出了一个新的QCD进化库，用于非偏度Parton分布函数：EKO。该程序求解DGLAP方程，直到临时到领先顺序。 EKO的独特功能是，可以存储并迅速应用于进化几个初始PDF的解决方案操作员的计算，它们独立于边界条件。 EKO方法将$ n $ -space解决方案的功率与$ x $ - 空间交付的灵活性相结合，从而可以轻松地与现有代码接口。该代码是完全开源的，并用Python编写，具有模块化结构，以促进使用，可读性和可能的扩展。我们提供一组具有类似可用工具的基准，找到良好的共识。
We present a new QCD evolution library for unpolarized parton distribution functions: EKO. The program solves DGLAP equations up to next-to-next-to-leading order. The unique feature of EKO is the computation of solution operators, which are independent of the boundary condition, can be stored and quickly applied to evolve several initial PDFs. The EKO approach combines the power of $N$-space solutions with the flexibility of a $x$-space delivery, that allows for an easy interface with existing codes. The code is fully open source and written in Python, with a modular structure in order to facilitate usage, readability and possible extensions. We provide a set of benchmarks with similar available tools, finding good agreement.
