High-multiplicity signatures at particle colliders can arise in Standard Model processes and beyond. With such signatures, difficulties often arise from the large dimensionality of the kinematic space. For final states containing a single type of particle signature, this results in a combinatorial problem that hides underlying kinematic information. We explore using a neural network that includes a Lorentz Layer to extract high-dimensional correlations. We use the case of squark decays in R-Parity-violating Supersymmetry as a benchmark, comparing the performance to that of classical methods. With this approach, we demonstrate significant improvement over traditional methods.
@article{arxiv.2201.02205,
title = {Solving Combinatorial Problems at Particle Colliders Using Machine Learning},
author = {Anthony Badea and William James Fawcett and John Huth and Teng Jian Khoo and Riccardo Poggi and Lawrence Lee},
journal= {arXiv preprint arXiv:2201.02205},
year = {2022}
}