  We study the potential of deep learning to resolve the combinatorial problem
in SUSY-like events with two invisible particles at the LHC. As a concrete
example, we focus on dileptonic $t \bar t$ events, where the combinatorial
problem becomes an issue of binary classification: pairing the correct lepton
with each $b$ quark coming from the decays of the tops. We investigate the
performance of a number of machine learning algorithms, including
attention-based networks, which have been used for a similar problem in the
fully-hadronic channel of $t\bar t$ production; and the Lorentz Boost Network,
which is motivated by physics principles. We then consider the general case
when the underlying mass spectrum is unknown, and hence no kinematic endpoint
information is available. Compared against existing methods based on kinematic
variables, we demonstrate that the efficiency for selecting the correct pairing
is greatly improved by utilizing deep learning techniques.
