Pineline: Industrialization of high-energy theory predictions

We present a collection of tools efficiently automating the computation of large sets of theory predictions for high-energy physics. Calculating predictions for different processes often require dedicated programs. These programs, however, accept inputs and produce outputs that are usually very different from each other. The industrialization of theory predictions is achieved by a framework which harmonizes inputs (runcard, parameter settings), standardizes outputs (in the form of grids), produces reusable intermediate objects, and carefully tracks all meta data required to reproduce the computation. Parameter searches and fitting of non-perturbative objects are exemplary use cases that require a full or partial re-computation of theory predictions and will thus benefit of such a toolset. As an example application we present a study of the impact of replacing NNLO QCD K-factors with the exact NNLO predictions in a PDF fit.

本研究提出一套工具集，可高效自动化完成高能物理领域大规模理论预言的计算工作。针对不同物理过程的理论预言计算，通常需要使用专用程序，但这类程序的输入与输出格式往往存在显著差异。该框架通过统一输入格式（运行配置卡（runcard）、参数设置）、标准化输出格式（以网格（grids）形式）、生成可复用的中间对象，并严格追踪复现计算所需的全部元数据，实现了理论预言计算的工业化流程。参数扫描与非微扰物理对象的拟合是这类应用的典型场景：此类场景需要对理论预言进行全量或部分重新计算，因此可从该工具集中获益良多。作为应用示例，我们展示了一项研究：在部分子分布函数（PDF，Parton Distribution Functions）拟合中，用精确的次次领头阶（NNLO，Next-to-Next-to-Leading Order）量子色动力学（QCD，Quantum Chromodynamics）预言替换原有NNLO QCD K因子所产生的影响。