论文标题
在CERN大型强子对撞机上进行梁动力学研究的机器学习
Machine learning for beam dynamics studies at the CERN Large Hadron Collider
论文作者
论文摘要
机器学习需要多种技术，这些技术从几十年来一直在科学和工程中广泛使用。高能量物理学也从这些工具的功能中获利，用于对围栏数据进行高级分析。直到最近，机器学习才开始成功地应用于Accelerator Physics的领域，这是由全球几个实验室在该领域中部署在该领域中的强烈努力作证的。 CERN也是如此，最近，最近专注的努力专门用于将机器学习技术应用于大型强子对撞机（LHC）的光束动力学研究。这意味着从光束测量和机器性能优化到从跟踪非线性束动力学的模拟来分析数值数据的广泛应用。在本文中，详细介绍和讨论了当前涉及的LHC相关的应用程序，还要关注未来的发展。
Machine learning entails a broad range of techniques that have been widely used in Science and Engineering since decades. High-energy physics has also profited from the power of these tools for advanced analysis of colliders data. It is only up until recently that Machine Learning has started to be applied successfully in the domain of Accelerator Physics, which is testified by intense efforts deployed in this domain by several laboratories worldwide. This is also the case of CERN, where recently focused efforts have been devoted to the application of Machine Learning techniques to beam dynamics studies at the Large Hadron Collider (LHC). This implies a wide spectrum of applications from beam measurements and machine performance optimisation to analysis of numerical data from tracking simulations of non-linear beam dynamics. In this paper, the LHC-related applications that are currently pursued are presented and discussed in detail, paying also attention to future developments.
