GRAPES-3 Applies ML to Classify Muon and Hadron Tracks

Researchers from the GRAPES-3 collaboration at the Tata Institute of Fundamental Research have developed a machine learning pipeline to distinguish secondary muon and punch-through hadron tracks in the GRAPES-3 muon telescope. Using XGBoost, they achieved 88.7% accuracy for single-particle classification, and for multiparticle events, they employed a Graph Neural Network with edge convolution layers and a Dynamic Reduction Network for particle count estimation.

ML pipelines that combine per-hit classifiers with graph-based event models can improve particle identification in dense detector arrays, which matters for label-scarce, high-throughput instrumentation data. Per the arXiv abstract 2607.07263 and the PoS ICRC2025 conference paper, researchers from the GRAPES-3 collaboration Tata Institute of Fundamental Research present a machine learning pipeline to distinguish secondary muon and punch-through hadron tracks in the GRAPES-3 muon telescope . The study uses CORSIKA proton-shower simulations energy 100-158 TeV fed into a Geant4 detector simulation. Single-particle classifiers tested include decision trees, random forests, neural networks, and XGBoost , with XGBoost achieving the highest reported accuracy of 88.7% , per the authors. For multiparticle events the team models events as graphs and trains a Graph Neural Network with edge convolution layers for per-hit classification, followed by a regression using Dynamic Reduction Network to estimate particle counts, as described in the PoS paper.