Open Access
| Issue |
EPJ Web Conf.
Volume 337, 2025
27th International Conference on Computing in High Energy and Nuclear Physics (CHEP 2024)
|
|
|---|---|---|
| Article Number | 01349 | |
| Number of page(s) | 8 | |
| DOI | https://doi.org/10.1051/epjconf/202533701349 | |
| Published online | 07 October 2025 | |
- ATLAS Collaboration, The ATLAS Experiment at the CERN Large Hadron Collider, JINST 3, S08003 (2008). 10.1088/1748-0221/3/08/S08003 [Google Scholar]
- ATLAS Collaboration, Jet reconstruction and performance using particle flow with the ATLAS Detector, Eur. Phys. J. C 77, 466 (2017), 1703.10485. 10.1140/epjc/s10052-017-5031-2 [CrossRef] [PubMed] [Google Scholar]
- F.A. Di Bello, E. Dreyer, S. Ganguly, E. Gross, L. Heinrich, A. Ivina, M. Kado, N. Kakati, L. Santi, J. Shlomi et al., Reconstructing particles in jets using set transformer and hypergraph prediction networks, The European Physical Journal C 83, 596 (2023). 10.1140/epjc/s10052-023-11677-7 [CrossRef] [EDP Sciences] [PubMed] [Google Scholar]
- ATLAS Collaboration, Deep Learning for Pion Identification and Energy Calibration with the ATLAS Detector, ATL-PHYS-PUB-2020-018 (2020), https://cds.cern. ch/record/2724632 [Google Scholar]
- ATLAS Collaboration, Point Cloud Deep Learning Methods for Pion Reconstruction in the ATLAS Experiment, ATL-PHYS-PUB-2022-040 (2022), https://cds.cern.ch/record/2825379 [Google Scholar]
- ATLAS Collaboration, Software and computing for Run 3 of the ATLAS experiment at the LHC (2024), 2404.06335. [Google Scholar]
- M. Zaheer, S. Kottur, S. Ravanbhakhsh, B. Póczos, R. Salakhutdinov, A.J. Smola, Deep Sets, in Proceedings of the 31st International Conference on Neural Information Processing Systems (Curran Associates Inc., Red Hook, NY, USA, 2017), NIPS’17, p. 3394–3404, ISBN 9781510860964, 10.5555/3294996.3295098 [Google Scholar]
- F. Scarselli, M. Gori, A.C. Tsoi, M. Hagenbuchner, G. Monfardini, The graph neural network model, IEEE Trans. Neural Networks 20, 61 (2009). 10.1109/TNN.2008.2005605 [Google Scholar]
- C.R. Qi, H. Su, K. Mo, L.J. Guibas, Pointnet: Deep learning on point sets for 3d classification and segmentation, in 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (2017), pp. 77–85, 10.1109/CVPR.2017.16 [Google Scholar]
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