text: 348,99,1480358744512999434,11346882,Larry Lee,"New paper day! Getting it out after years of exploring this space — Tackling combinatorial problems at particle colliders with Machine Learning™. It’s full of action and intrigue, and I hope you take a look! (Also it’s pretty short and pretty pretty) <LINK> <LINK> Work largely performed by Anthony Badea of @harvardphysics. Also w/ @JohnHuth1, @CodeMonkey8, @RiccardoPoggi, Will Fawcett, and myself from @UTKPhysAstro",https://arxiv.org/abs/2201.02205,"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. ","Solving Combinatorial Problems at Particle Colliders Using Machine