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Graph neural networks enhance cosmic-ray detection accuracy

By PulseAugur Editorial · [1 sources] ·

Researchers have developed a novel method using deep ensemble graph neural networks to reconstruct the direction and energy of cosmic rays detected by radio arrays. By representing triggered antennas as a graph structure and integrating physical knowledge into the GNN, the approach enhances precision and reduces the need for extensive training data. The method achieves a 0.092-degree angular resolution and 16.4% energy reconstruction resolution on simulated data, while also incorporating uncertainty estimation for improved reliability. AI

IMPACT This research demonstrates how graph neural networks can improve the accuracy and efficiency of scientific data analysis in fields like astrophysics.

RANK_REASON Academic paper detailing a new methodology for scientific data analysis. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.LG →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

Graph neural networks enhance cosmic-ray detection accuracy

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Academic paper detailing a new methodology for scientific data analysis. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.LG TIER_1 English(EN) · Ars\`ene Ferri\`ere, Aur\'elien Benoit-L\'evy, Olivier Martineau-Huynh, Mat\'ias Tueros ·

    Reconstruction of cosmic-ray direction and energy in radio arrays using deep ensemble graph neural networks

    arXiv:2602.23321v2 Announce Type: replace-cross Abstract: Using advanced machine learning techniques, we developed a method to reconstruct the arrival direction and energy of ultra-high-energy cosmic rays from the voltage traces they induce on ground-based radio detector arrays. …

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