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Kinematic variables have been playing an important role in colliderphenomenology, as they expedite discoveries of new particles by separatingsignal events from unwanted background events and allow for measurements ofparticle properties such as masses, couplings, spins, etc. For the past 10years, an enormous number of kinematic variables have been designed andproposed, primarily for the experiments at the Large Hadron Collider, allowingfor a drastic reduction of high-dimensional experimental data tolower-dimensional observables, from which one can readily extract underlyingfeatures of phase space and develop better-optimized data-analysis strategies.We review these recent developments in the area of phase space kinematics,summarizing the new kinematic variables with important phenomenologicalimplications and physics applications. We also review recently proposedanalysis methods and techniques specifically designed to leverage the newkinematic variables. As machine learning is nowadays percolating through manyfields of particle physics including collider phenomenology, we discuss theinterconnection and mutual complementarity of kinematic variables and machinelearning techniques. We finally discuss how the utilization of kinematicvariables originally developed for colliders can be extended to otherhigh-energy physics experiments including neutrino experiments.

Kinematic Variables and Feature Engineering for Particle Phenomenology