Applying self-organizing maps to nonresonant searches

An excess of events seen in a nonresonant search at a collider can be difficult to ascribe to a specific hypothesis from amongst several competing ones. We explore a novel approach of applying self-organizing maps to such an excess to uniquely identify a hypothesis. We use a search for vector-like leptons in a trilepton final state to outline the method. We define an approach combining the inherent clustering of these maps and elements of supervised learning. We compare the performance of this approach with a multiclassifying neural network. We find that the method using self-organizing maps competes well (despite not using any standard model processes in the training), and provides additional tools that would help characterize any observed excesses in searches.