论文标题
发电机和（加速）未来
Generators and the (Accelerated) Future
论文作者
论文摘要
随着高光度LHC在不久的将来上网，事件生成器将需要提供非常大的事件样本以匹配实验精度。目前，产生这些事件的估计成本超过了LHC实验的计算预算。为了解决这些问题，需要提高事件发生器的计算效率。正在采取许多不同的方法来实现这一目标。我将介绍在GPU上实施事件生成器的持续工作，机器学习矩阵元素，计算机学习相位空间并最大程度地减少负重事件的数量。
With the High Luminosity LHC coming online in the near future, event generators will need to provide very large event samples to match the experimental precision. Currently, the estimated cost to generate these events exceeds the computing budget of the LHC experiments. To address these issues, the computing efficiency of event generators need to be improved. Many different approaches are being taken to achieve this goal. I will cover the ongoing work on implementing event generators on the GPUs, machine learning the matrix element, machine learning the phase space, and minimizing the number of negative weight events.
