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Cited article:

Trackformers: in search of transformer-based particle tracking for the high-luminosity LHC era

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The European Physical Journal C 85 (4) (2025)
https://doi.org/10.1140/epjc/s10052-025-14156-3

End-to-End Multi-track Reconstruction Using Graph Neural Networks at Belle II

L. Reuter, G. De Pietro, S. Stefkova, T. Ferber, V. Bertacchi, G. Casarosa, L. Corona, P. Ecker, A. Glazov, Y. Han, M. Laurenza, T. Lueck, L. Massaccesi, S. Mondal, B. Scavino, S. Spataro, C. Wessel and L. Zani
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Geometric Graph Neural Network based track finding

Dolores Garcia, Brieuc Francois, Michele Selvaggi, T. Szumlak, B. Rachwał, A. Dziurda, M. Schulz, D. vom Bruch, K. Ellis and S. Hageboeck
EPJ Web of Conferences 337 01125 (2025)
https://doi.org/10.1051/epjconf/202533701125

Efficient ML-Assisted Particle Track Reconstruction Designs

Sascha Caron, Nadezhda Dobreva, Antonio Ferrer Sánchez, José D. Martín-Guerrero, Uraz Odyurt, Roberto Ruiz de Austri Bazan, Zef Wolffs, Yue Zhao, T. Szumlak, B. Rachwał, A. Dziurda, M. Schulz, D. vom Bruch, K. Ellis and S. Hageboeck
EPJ Web of Conferences 337 01299 (2025)
https://doi.org/10.1051/epjconf/202533701299

Track Reconstruction Transformer: A Scalable Deep Learning Approach for Particle Tracking in High-Energy Physics

A. Nikolskaia, P. Goncharov, G. Ososkov, D. Girdyuk and D. Rusov
Physics of Particles and Nuclei 56 (6) 1555 (2025)
https://doi.org/10.1134/S1063779625700856

Graph neural network for neutrino physics event reconstruction

A. Aurisano, V. Hewes, G. Cerati, J. Kowalkowski, C. S. Lee, W. Liao, D. Grzenda, K. Gumpula and X. Zhang
Physical Review D 110 (3) (2024)
https://doi.org/10.1103/PhysRevD.110.032008