The GRAPES-3 experiment is a ground-based extensive air shower array which consists of
approximately 400 closely packed plastic scintillator detectors and a large area muon telescope.
Estimating the number of associated muons created in an air shower is crucial to understand
the properties of primary cosmic rays. The GRAPES-3 muon telescope (G3MT) records these
secondary muons, however, the punch-through hadrons can introduce background noise. This
study aims to develop a machine learning pipeline to distinguish the tracks of secondary muons
and hadrons at G3MT. We have used CORSIKA-simulated proton showers having energy in the
range 100–158 TeV as an input for a Geant4-based detector simulation to analyze the signatures of
both type of particles. Initially, single-particle classification was performed using decision trees,
random forests, neural networks, and XGBoost, with XGBoost achieving the highest accuracy of
88.7%. To model Graph Neural Networks (GNNs) each event was represented as a graph with
detector hits modeled as nodes. A GNN with edge convolution layers was developed to classify
each node as a muon or hadron hit. Following this, a deep learning regression model using
dynamic reduction network was developed to estimate the number of particles and muons striking
G3MT simultaneously. Details of the analysis and results of the multiparticle classification task
will be presented.