Use of a Generalized Energy Mover's Distance in the Search for Rare Phenomena at Colliders

In this paper, we expand on the previously proposed concept of Energy Mover's Distance. The resulting observables are shown to provide a way of identifying rare processes in proton-proton collider experiments. It is shown that different processes are grouped together differently and that this can contribute to the improvement of experimental analyses. The $t\bar{t}Z$ production at the Large Hadron Collider is used as a benchmark to illustrate the applicability of the method. Furthermore, we study the use of these observables as new features which can be used in the training of Deep Neural Networks.