论文标题
机器学习和LHC活动一代
Machine Learning and LHC Event Generation
论文作者
论文摘要
第一原则模拟是高能物理研究计划的核心。他们将多功能探测器的庞大数据输出与基本理论预测和解释联系起来。这篇综述说明了现代机器学习到基于事件的推理的广泛应用，包括由粒子物理的特定要求驱动的构思发展。在粒子物理和机器学习界面开发的新想法和工具将提高正向模拟的速度和精度，处理碰撞数据的复杂性，并增强推理作为反向模拟问题。
First-principle simulations are at the heart of the high-energy physics research program. They link the vast data output of multi-purpose detectors with fundamental theory predictions and interpretation. This review illustrates a wide range of applications of modern machine learning to event generation and simulation-based inference, including conceptional developments driven by the specific requirements of particle physics. New ideas and tools developed at the interface of particle physics and machine learning will improve the speed and precision of forward simulations, handle the complexity of collision data, and enhance inference as an inverse simulation problem.
