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arXiv 1808.07802hep-ph
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用神经网络重新加权部分子簇射：终态情况

Reweighting a parton shower using a neural network: the final-state case

Enrico Bothmann, Luigi Del Debbio

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additional references added in introduction
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摘要：

使用QCD计算，其中包括通过部分子簇射算法的软共线矩阵的重新确认，目前是不可能的PDF拟合，由于计算成本高的PDF的每个变化的评估可观的。不幸的是，插值方法，否则适用于克服这个问题是不容易推广到所有阶部分子簇射贡献。相反，我们提出了一个近似训练的神经网络的基础上预测的影响，不同的输入参数的部分子簇射的截面在一个给定的可观察的bin，插值之间的变化的训练数据集。这第一次出版物的重点是提供一个证明的原则的方法，通过改变淋浴依赖于$α_\text{S}$的简化淋浴模型和一个完整的淋浴实现三个不同的观测，领先的排放规模，排放量和推力事件的形状。扩展到PDF依赖的初始状态淋浴演化，这是需要的应用程序的PDF适合是留给即将出版的。

英文摘要：

The use of QCD calculations that include the resummation of soft-collinear logarithms via parton-shower algorithms is currently not possible in PDF fits due to the high computational cost of evaluating observables for each variation of the PDFs. Unfortunately the interpolation methods that are otherwise applied to overcome this issue are not readily generalised to all-order parton-shower contributions. Instead, we propose an approximation based on training a neural network to predict the effect of varying the input parameters of a parton shower on the cross section in a given observable bin, interpolating between the variations of a training data set. This first publication focuses on providing a proof-of-principle for the method, by varying the shower dependence on $α_\text{S}$ for both a simplified shower model and a complete shower implementation for three different observables, the leading emission scale, the number of emissions and the Thrust event shape. The extension to the PDF dependence of the initial-state shower evolution that is needed for the application to PDF fits is left to a forthcoming publication.

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。

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