Kinematic variables play an important role in collider phenomenology, as they expedite discoveries of new particles by separating signal events from unwanted background events and allow for measurements of particle properties such as masses, couplings, and spins. For the past ten years, an enormous number of kinematic variables have been designed and proposed, primarily for the experiments at the CERN Large Hadron Collider, allowing for a drastic reduction of high-dimensional experimental data to lower-dimensional observables, from which one can readily extract underlying features of phase space and develop better-optimized data-analysis strategies. Recent developments in the area of phase-space kinematics are reviewd, and new kinematic variables with important phenomenological implications and physics applications are summarized. Recently proposed analysis methods and techniques specifically designed to leverage new kinematic variables are also reviewed. As machine learning is currently percolating through many fields of particle physics, including collider phenomenology, the interconnection and mutual complementarity of kinematic variables and machine-learning techniques are discussed. Finally, the manner in which utilization of kinematic variables originally developed for colliders can be extended to other high-energy physics experiments, including neutrino experiments, is discussed.

中文翻译：

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粒子现象学的运动学变量和特征工程

运动变量在对撞机现象学中发挥着重要作用，因为它们通过将信号事件与不需要的背景事件分开来加速新粒子的发现，并允许测量粒子属性，例如质量、耦合和自旋。在过去的十年里，大量的运动学变量被设计和提出，主要是为了欧洲核子研究中心大型强子对撞机的实验，允许将高维实验数据急剧减少为低维可观测数据，从中可以轻松提取相空间的基本特征并开发更好优化的数据分析策略。回顾了相空间运动学领域的最新进展，总结了具有重要唯象意义和物理应用的新运动学变量。还回顾了最近提出的专门用于利用新运动学变量的分析方法和技术。由于机器学习目前正在渗透到粒子物理学的许多领域，包括对撞机现象学，因此讨论了运动学变量和机器学习技术的互连和相互补充。最后，讨论了最初为对撞机开发的运动学变量的利用可以扩展到其他高能物理实验（包括中微子实验）的方式。