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

Resolving Combinatorial Ambiguities in Dilepton $t \bar t$ Event Topologies with Neural Networks