Resolving combinatorial ambiguities in dilepton t t ¯ event topologies with neural networks

Abstract

We study the potential of deep learning to resolve the combinatorial problem in supersymmetrylike events with two invisible particles at the LHC. As a concrete example, we focus on dileptonic tt¯ 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 tt¯ 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 end point 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.