  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.
