Particle Track Reconstruction using Geometric Deep Learning

TL;DR

This paper introduces a novel geometric deep learning algorithm for particle track reconstruction in cosmic ray detection, leveraging graphical structures to improve accuracy and robustness in 3-D scintillator detectors.

Contribution

It presents a new deep learning-based tracking method that incorporates domain knowledge through graph structures, specifically designed for cosmic ray muon detection in 3-D detectors.

Findings

The algorithm demonstrates good performance in simulated environments.

It shows robustness to noise and double hits.

Potential for application in various astrophysical and collider detectors.

Abstract

Muons are the most abundant charged particles arriving at sea level originating from the decay of secondary charged pions and kaons. These secondary particles are created when high-energy cosmic rays hit the atmosphere interacting with air nuclei initiating cascades of secondary particles which led to the formation of extensive air showers (EAS). They carry essential information about the extra-terrestrial events and are characterized by large flux and varying angular distribution. To account for open questions and the origin of cosmic rays, one needs to study various components of cosmic rays with energy and arriving direction. Because of the close relation between muon and neutrino production, it is the most important particle to keep track of. We propose a novel tracking algorithm based on the Geometric Deep Learning approach using graphical structure to incorporate domain knowledge to track cosmic ray muons in our 3-D scintillator detector. The detector is modeled using the GEANT4 simulation package and EAS is simulated using CORSIKA (COsmic Ray SImulations for KAscade) with a focus on muons originating from EAS. We shed some light on the performance, robustness towards noise and double hits, limitations, and application of the proposed algorithm in tracking applications with the possibility to generalize to other detectors for astrophysical and collider experiments.