Solving Combinatorial Problems at Particle Colliders Using Machine Learning

High-multiplicity signatures at particle colliders can be given by Standard Model as well as BSM processes. In such signatures, difficulties arise from the large dimensionality of the kinematic space. For final states of indistinguishable particle signatures, this results in a large combinatorial problem that hides underlying kinematic information. We explore using a neural network that includes a Lorentz Layer to effectively extract high-dimensional correlations. We use the case of squark decays in RPV SUSY as a benchmark, comparing the performance to that of classical methods. We demonstrate significant improvements.