论文标题
使用神经网络有效评估高多样性散射幅度
Using neural networks for efficient evaluation of high multiplicity scattering amplitudes
论文作者
论文摘要
高度多样性散射的精确理论预测取决于对越来越复杂的散射幅度的评估，而散射幅度却具有极高的CPU成本。对于最先进的过程，这可能会导致完全差异分布的技术瓶颈。在本文中，我们探讨了使用神经网络近似多变量散射幅度的可能性，并为蒙特卡洛整合提供有效的输入。我们专注于QCD校正到$ e^+e^ - \ to $ jets to $ to-jets to One-loop和最多五个喷气机。当对一系列网络进行训练以分为由其红外奇异性结构定义的部门时，我们证明了可靠的插值。一环分布的完整仿真显示了至少在标准方法上的数量级的速度提高。
Precision theoretical predictions for high multiplicity scattering rely on the evaluation of increasingly complicated scattering amplitudes which come with an extremely high CPU cost. For state-of-the-art processes this can cause technical bottlenecks in the production of fully differential distributions. In this article we explore the possibility of using neural networks to approximate multi-variable scattering amplitudes and provide efficient inputs for Monte Carlo integration. We focus on QCD corrections to $e^+e^-\to$ jets up to one-loop and up to five jets. We demonstrate reliable interpolation when a series of networks are trained to amplitudes that have been divided into sectors defined by their infrared singularity structure. Complete simulations for one-loop distributions show speed improvements of at least an order of magnitude over a standard approach.
