Dissecting Jets and Missing Energy Searches Using $n$-body Extended Simplified Models

TL;DR

This paper introduces an $n$-body extension of Simplified Models to better characterize multijet plus missing energy signals at the LHC, optimizing observable combinations for improved background discrimination.

Contribution

It presents the first application of $n$-body extended Simplified Models in multijet plus missing energy searches, analyzing optimal observable sets for signal-background discrimination.

Findings

Including observables from energy, energy scale, and energy structure classes enhances sensitivity.

Non-trivial correlations between variables significantly improve discrimination power.

Boosted decision trees effectively classify signal versus background using combined observables.

Abstract

Simplified Models are a useful way to characterize new physics scenarios for the LHC. Particle decays are often represented using non-renormalizable operators that involve the minimal number of fields required by symmetries. Generalizing to a wider class of decay operators allows one to model a variety of final states. This approach, which we dub the $n$-body extension of Simplified Models, provides a unifying treatment of the signal phase space resulting from a variety of signals. In this paper, we present the first application of this framework in the context of multijet plus missing energy searches. The main result of this work is a global performance study with the goal of identifying which set of observables yields the best discriminating power against the largest Standard Model backgrounds for a wide range of signal jet multiplicities. Our analysis compares combinations of one, two and three variables, placing emphasis on the enhanced sensitivity gain resulting from non-trivial correlations. Utilizing boosted decision trees, we compare and classify the performance of missing energy, energy scale and energy structure observables. We demonstrate that including an observable from each of these three classes is required to achieve optimal performance. This work additionally serves to establish the utility of $n$-body extended Simplified Models as a diagnostic for unpacking the relative merits of different search strategies, thereby motivating their application to new physics signatures beyond jets and missing energy.