Anja Butter, Tomas Jezo, Michael Klasen, Mathias Kuschick, Sofia Palacios Schweitzer, Tilman Plehn
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引用次数: 0
摘要
大型强子对撞机背景中的壳外效应对精确预测至关重要，同时也是模拟的挑战。我们提出了一种基于扩散神经网络转换高维分布的新方法，并用它从简单得多的壳上运动学过程生成壳外运动学过程。我们将其应用于一个在 LO 产生顶对的玩具例子，展示了我们的方法如何快速而精确地生成壳外构型，同时再现甚至具有挑战性的壳内特征。
Off-shell effects in large LHC backgrounds are crucial for precision predictions and, at the same time, challenging to simulate. We present a novel method to transform high-dimensional distributions based on a diffusion neural network and use it to generate a process with off-shell kinematics from the much simpler on-shell one. Applied to a toy example of top pair production at LO we show how our method generates off-shell configurations fast and precisely, while reproducing even challenging on-shell features.
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