使用规范化流生成事件
Event Generation with Normalizing Flows
摘要：
本文提出了一种基于归一化流的积分器，可用于提高蒙特卡罗事件发生器的减重效率。与基于代理模型的机器学习方法相比，即使底层神经网络没有经过最佳训练，我们的方法也会生成正确的结果。我们使用Drell-Yan型过程在LHC的例子，在领导和部分在次领导阶QCD的新策略。
英文摘要：
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.
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