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Электронный каталог: Omelianchuk, S. - Graph Attention Neural Network for Clustering Particle Tracks by Events in the SPD Experiment
Omelianchuk, S. - Graph Attention Neural Network for Clustering Particle Tracks by Events in the SPD Experiment
Статья
Автор: Omelianchuk, S.
The European Physical Journal C: Graph Attention Neural Network for Clustering Particle Tracks by Events in the SPD Experiment
б.г.
ISBN отсутствует
Автор: Omelianchuk, S.
The European Physical Journal C: Graph Attention Neural Network for Clustering Particle Tracks by Events in the SPD Experiment
б.г.
ISBN отсутствует
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Статья
Omelianchuk, S.
Graph Attention Neural Network for Clustering Particle Tracks by Events in the SPD Experiment / S.Omelianchuk, Y.Talochka, N.Voytishin, G.Ososkov. – Text : electronic // The European Physical Journal C. – 2026. – Vol. 86, No. 7. – P. 789. – URL: https://doi.org/10.1140/epjc/s10052-026-16033-z. – Bibliogr.: 17.
A Graph Attention Neural Network (GANN) for particle track clustering by events in time slices obtained at the Spin Physics Detector (SPD) of the Nuclotron-based Ion Collider fAcility (NICA) is presented. A novel approach is applied to time slice processing at high particle multiplicity and pile-up. The model is trained using a hierarchical approach and evaluated on time slices generated by the Monte Carlo technique according to the SPD experiment configuration. Additionally, the model is trained and evaluated on time slices generated using the TrackML dataset to analyze its stability. The model architecture includes a graph encoder and an edge classifier, both of which use the message-passing principle and iteratively structured approach. The GANN exhibits high performance in track clustering by events for the SPD dataset, achieving 99% accuracy and 95% precision and recall.
ОИЯИ = ОИЯИ (JINR)2026
Omelianchuk, S.
Graph Attention Neural Network for Clustering Particle Tracks by Events in the SPD Experiment / S.Omelianchuk, Y.Talochka, N.Voytishin, G.Ososkov. – Text : electronic // The European Physical Journal C. – 2026. – Vol. 86, No. 7. – P. 789. – URL: https://doi.org/10.1140/epjc/s10052-026-16033-z. – Bibliogr.: 17.
A Graph Attention Neural Network (GANN) for particle track clustering by events in time slices obtained at the Spin Physics Detector (SPD) of the Nuclotron-based Ion Collider fAcility (NICA) is presented. A novel approach is applied to time slice processing at high particle multiplicity and pile-up. The model is trained using a hierarchical approach and evaluated on time slices generated by the Monte Carlo technique according to the SPD experiment configuration. Additionally, the model is trained and evaluated on time slices generated using the TrackML dataset to analyze its stability. The model architecture includes a graph encoder and an edge classifier, both of which use the message-passing principle and iteratively structured approach. The GANN exhibits high performance in track clustering by events for the SPD dataset, achieving 99% accuracy and 95% precision and recall.
ОИЯИ = ОИЯИ (JINR)2026
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