  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.
