# GRAPES-3 Applies ML to Classify Muon and Hadron Tracks

> Source: <https://letsdatascience.com/news/grapes-3-applies-ml-to-classify-muon-and-hadron-tracks-f2c6dd94>
> Published: 2026-07-09 04:00:00+00:00

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.