Learning How to Count: A High Multiplicity Search for the LHC

TL;DR

This paper presents a novel high multiplicity search method at the LHC that uses jet substructure to count subjets, improving sensitivity to diverse signals with large final states and reducing reliance on missing energy.

Contribution

It introduces a new technique combining jet clustering and substructure analysis to detect a broad class of high multiplicity signals at the LHC.

Findings

Enhanced sensitivity to various decay topologies.

Reduced dependence on missing energy for background suppression.

Data-driven background estimation method.

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.