Learning how to count: A high multiplicity search for the LHC

Abstract

We introduce a search technique that is sensitive to a broad class of signals with large final state multiplicities. Events are clustered into large radius jets and jet substructure techniques are used to count the number of subjets within each jet. The search consists of a cut on the total number of subjets in the event as well as the summed jet mass and missing energy. Two different techniques for counting subjets are described and expected sensitivities are presented for eight benchmark signals. These signals exhibit diverse phenomenology, including 2-step cascade decays, direct three body decays, and multi-top final states. We find improved sensitivity to these signals as compared to previous high multiplicity searches as well as a reduced reliance on missing energy requirements. One benefit of this approach is that it allows for natural data driven estimates of the QCD background. © 2013 SISSA.