Graph Neural Networks for Particle Reconstruction in High Energy Physics detectors

Understand

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.