Speaker
Description
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%. Following this, a deep learning regression model was developed to estimate the number of particles striking G3MT simultaneously. Combining these models, we performed multiparticle identification by comparing Long Short-Term Memory(LSTM), deep neural networks, and graph-based neural networks to identify the tracks of multiple simultaneous muon and hadron events. Details of the analysis and results of the multiparticle classification task will be presented.
| Collaboration(s) | GRAPES-3 |
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