论文标题
归一流的活动产生
Event Generation with Normalizing Flows
论文作者
论文摘要
我们提出了一种基于标准化流的新颖集成器，可用于提高对撞机物理模拟的蒙特卡洛事件发生器的重量效率。与基于替代模型的机器学习方法相反，即使基础神经网络没有经过最佳培训，我们的方法也会生成正确的结果。我们以LHC处的Drell-Yan类型过程的示例来体现新策略，无论是在领先还是在近代领先顺序的QCD处部分。
We present a novel integrator based on normalizing flows which can be used to improve the unweighting efficiency of Monte-Carlo event generators for collider physics simulations. In contrast to machine learning approaches based on surrogate models, our method generates the correct result even if the underlying neural networks are not optimally trained. We exemplify the new strategy using the example of Drell-Yan type processes at the LHC, both at leading and partially at next-to-leading order QCD.
