论文标题
如何使事件不重要
How to GAN Event Unweighting
论文作者
论文摘要
神经网络的活动产生最近取得了重大进展。最大的开放问题仍然是，这种新方法将如何将LHC模拟加速到即将进行的LHC运行所需的水平。我们针对已知的标准模拟瓶颈，并展示如何通过生成网络改善其未加权程序。这可能会导致模拟速度的显着增长。
Event generation with neural networks has seen significant progress recently. The big open question is still how such new methods will accelerate LHC simulations to the level required by upcoming LHC runs. We target a known bottleneck of standard simulations and show how their unweighting procedure can be improved by generative networks. This can, potentially, lead to a very significant gain in simulation speed.
