Machine Learning Techniques for Intermediate Mass Gap Lepton Partner Searches at the Large Hadron Collider

Abstract

We consider machine learning techniques associated with the application of a Boosted Decision Tree (BDT) to searches at the Large Hadron Collider (LHC) for pair-produced lepton partners which decay to leptons and invisible particles. This scenario can arise in the Minimal Supersymmetric Standard Model (MSSM), but can be realized in many other extensions of the Standard Model (SM). We focus on the case of intermediate mass splitting ($\sim 30~{\rm GeV}$) between the dark matter (DM) and the scalar. For these mass splittings, the LHC has made little improvement over LEP due to large electroweak backgrounds. We find that the use of machine learning techniques can push the LHC well past discovery sensitivity for a benchmark model with a lepton partner mass of $\sim 110~{\rm GeV}$, for an integrated luminosity of $300~{\rm fb}^{-1}$, with a signal-to-background ratio of $\sim 0.3$. The LHC could exclude models with a lepton partner mass as large as $\sim 160~{\rm GeV}$ with the same luminosity. The use of machine learning techniques in searches for scalar lepton partners at the LHC could thus definitively probe the parameter space of the MSSM in which scalar muon mediated interactions between SM muons and Majorana singlet DM can both deplete the relic density through dark matter annihilation and satisfy the recently measured anomalous magnetic moment of the muon. We identify several machine learning techniques which can be useful in other LHC searches involving large and complex backgrounds.