{"slug": "gnn-based-energy-reconstruction-pipeline-for-grapes-3", "title": "GNN-Based Energy Reconstruction Pipeline for GRAPES-3", "summary": "Researchers at the GRAPES-3 experiment developed a graph neural network (GNN)-based pipeline for cosmic ray energy reconstruction, achieving improved accuracy and automated feature extraction on sparse detector arrays. The pipeline, detailed in a July 2026 arXiv paper and ICRC2025 poster, uses hierarchical dynamic GNNs and feature selection to enhance energy resolution for multiple mass groups.", "body_md": "For ML practitioners, this work demonstrates how graph-based deep learning can be applied to sparse, irregular detector arrays to improve energy reconstruction accuracy and feature automation, offering transferable techniques for other sensor-network experiments. According to the arXiv entry, the paper \"The Deep Learning Cosmic Ray Energy Reconstruction Pipeline for the GRAPES-3 Experiment\" was submitted on 8 July 2026 by Sambit Sarkar and one co-author (arXiv:2607.07265). Per the ICRC2025 poster hosted on Indico, the authors implemented a modular, hierarchical `dynamic GNN` reconstruction pipeline and compared multiple fine-tuning strategies. The arXiv paper and the poster report that the **GRAPES-3** array comprises **400** scintillator detectors at **8 m** spacing covering **25000 m^2**, plus a muon detector of **3712** proportional counters (arXiv; Indico). The authors trained and validated models on logarithmically binned showers for hydrogen, helium, nitrogen, aluminium and iron mass groups and used mutual information and F-statistic feature-selection to improve energy-resolution and mitigate large shower-core-distance effects (arXiv; Indico).", "url": "https://wpnews.pro/news/gnn-based-energy-reconstruction-pipeline-for-grapes-3", "canonical_source": "https://letsdatascience.com/news/gnn-based-energy-reconstruction-pipeline-for-grapes-3-f5e87d31", "published_at": "2026-07-09 04:00:00+00:00", "updated_at": "2026-07-09 05:18:03.441564+00:00", "lang": "en", "topics": ["machine-learning", "neural-networks", "ai-research"], "entities": ["GRAPES-3", "Sambit Sarkar", "arXiv"], "alternates": {"html": "https://wpnews.pro/news/gnn-based-energy-reconstruction-pipeline-for-grapes-3", "markdown": "https://wpnews.pro/news/gnn-based-energy-reconstruction-pipeline-for-grapes-3.md", "text": "https://wpnews.pro/news/gnn-based-energy-reconstruction-pipeline-for-grapes-3.txt", "jsonld": "https://wpnews.pro/news/gnn-based-energy-reconstruction-pipeline-for-grapes-3.jsonld"}}