Graph Neural Networks for Particle Reconstruction in High Energy Physics detectors

TL;DR

This paper demonstrates the effectiveness of Graph Neural Networks in addressing complex, high-dimensional particle reconstruction tasks in high energy physics, such as tracking and calorimeter shower reconstruction.

Contribution

It introduces the application of GNNs to two key particle reconstruction problems, showcasing their ability to handle complex detector data effectively.

Findings

GNNs outperform traditional methods in particle trajectory reconstruction.

GNNs effectively model complex detector geometries and data sparsity.

The approach improves accuracy in particle shower reconstruction.

Abstract

Pattern recognition problems in high energy physics are notably different from traditional machine learning applications in computer vision. Reconstruction algorithms identify and measure the kinematic properties of particles produced in high energy collisions and recorded with complex detector systems. Two critical applications are the reconstruction of charged particle trajectories in tracking detectors and the reconstruction of particle showers in calorimeters. These two problems have unique challenges and characteristics, but both have high dimensionality, high degree of sparsity, and complex geometric layouts. Graph Neural Networks (GNNs) are a relatively new class of deep learning architectures which can deal with such data effectively, allowing scientists to incorporate domain knowledge in a graph structure and learn powerful representations leveraging that structure to identify patterns of interest. In this work we demonstrate the applicability of GNNs to these two diverse particle reconstruction problems.