Pineline：高能物理理论预测的工业化
Pineline: Industrialization of High-Energy Theory Predictions
AI总结：
本文提出Pineline框架，通过统一输入、标准化输出和追踪元数据，实现高能物理理论预测计算的工业化，并展示了其在PDF拟合中替换NNLO QCD K-factors的应用。
摘要：
我们提出了一套自动化高效计算大量高能物理理论预测的工具集。不同过程的预测计算通常需要专用程序，而这些程序的输入与输出格式往往差异巨大。理论预测的工业化通过一个框架实现，该框架统一输入（runcard、参数设置），标准化输出（以grids形式），生成可复用的中间对象，并仔细追踪复现计算所需的所有元数据。参数搜索和非微扰对象的拟合是典型的用例，它们需要对理论预测进行全部或部分重新计算，因此将从该工具集中受益。作为示例应用，我们研究了在PDF拟合中用精确的NNLO预测替换NNLO QCD K-factors的影响。
英文摘要：
We present a collection of tools automating the efficient 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 in a PDF fit with the exact NNLO predictions.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
