论文标题
可逆的网络或参与者以探测器并再次返回
Invertible Networks or Partons to Detector and Back Again
论文作者
论文摘要
对于向前和反向具有物理含义的模拟，可逆神经网络特别有用。有条件的INN可以根据高级可观察物，专门针对LHC的ZW生产而倒转检测器模拟。它允许进行每个事实的统计解释。接下来，我们允许数量的QCD喷射数量。我们将检测器效应和QCD辐射展开到预定义的硬过程，同样在Parton级相位空间上进行了概率的解释。
For simulations where the forward and the inverse directions have a physics meaning, invertible neural networks are especially useful. A conditional INN can invert a detector simulation in terms of high-level observables, specifically for ZW production at the LHC. It allows for a per-event statistical interpretation. Next, we allow for a variable number of QCD jets. We unfold detector effects and QCD radiation to a pre-defined hard process, again with a per-event probabilistic interpretation over parton-level phase space.
