Solving Combinatorial Problems at Particle Colliders Using Machine Learning

Understand

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.