Solving Combinatorial Problems at Particle Colliders Using Machine Learning

TL;DR

This paper introduces a neural network with a Lorentz Layer to effectively solve combinatorial problems in high-dimensional kinematic spaces at particle colliders, demonstrating superior performance over classical methods.

Contribution

The paper presents a novel neural network architecture incorporating a Lorentz Layer for analyzing complex collider data, improving upon traditional combinatorial analysis techniques.

Findings

Neural network with Lorentz Layer outperforms classical methods.

Significant improvement in extracting kinematic correlations.

Effective in analyzing high-multiplicity collider signatures.

Abstract

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.