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 ttZ 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.

Automated summary

In this paper, the concept of energy Mover's distance is further developed and applied to the identification of rare processes in proton-proton collider experiments, with the ttZ production at the Large Hadron Collider as a benchmark. The resulting observables are found to improve experimental analyses and can be used as new features in deep neural network training.