Machine learning (ML) is a rapidly growing area of research in the field of particle physics, with a vast array of applications at the CERN LHC. ML has changed the way particle physicists conduct searches and measurements as a versatile tool used to improve existing approaches and enable fundamentally new ones. In these proceedings, we describe novel ML techniques and recent results for improved classification, fast simulation, unfolding, and anomaly detection in LHC experiments.

翻译：机器学习（ML）是粒子物理学领域中一个快速发展的研究方向，在欧洲核子研究中心大型强子对撞机（LHC）上具有广泛的应用。作为一种多功能工具，机器学习改变了粒子物理学家进行搜索和测量的方式，既用于改进现有方法，也催生了全新的研究范式。本文总结了LHC实验中用于改进分类、快速模拟、反卷积以及异常检测的新型机器学习技术与最新研究成果。