Open Access
| Issue |
EPJ Web Conf.
Volume 370, 2026
International Conference on Advanced Physics: Innovations for a Sustainable Future (IEMPHYS-26)
|
|
|---|---|---|
| Article Number | 01032 | |
| Number of page(s) | 10 | |
| DOI | https://doi.org/10.1051/epjconf/202637001032 | |
| Published online | 29 May 2026 | |
- C. Amrouche et al., The TrackML challenge: the Kaggle high-energy physics machine learning pseudo-competition. Comput. Softw. Big Sci. doi: https://doi.org/10.1051/epjconf/201921406037 [Google Scholar]
- X. Ju et al., Performance of a geometric deep learning pipeline for HL-LHC particle tracking. Eur. Phys. J. C 81, 876 (2021) doi: https://doi.org/10.48550/arXiv.2103.06995 [CrossRef] [Google Scholar]
- S. Van Stroud et al., Transformers for charged particle tracking in high energy physics. Mach. Learn.: Sci. Technol. 4, 025026 (2023) doi: https://doi.org/10.48550/arXiv.2411.07149 [Google Scholar]
- R.E. Kalman, A new approach to linear filtering and prediction problems. J. Basic Eng. 82, 35 (1960) doi: https://doi.org/10.1115/L3662552 [Google Scholar]
- A. Vaswani et al., Attention is all you need, in Proceedings of the 31st International Conference on Neural Information Processing Systems (NIPS'17), Long Beach, CA, USA, December 4-9 (2017), 6000-6010 doi: https://doi.org/10.48550/arXiv.1706.03762 [Google Scholar]
- A. Dosovitskiy et al., An image is worth 16x16 words: transformers for image recognition at scale, in Proceedings of the International Conference on Learning Representations (ICLR), Vienna, Austria, May 3-7 2021 doi: https://doi.org/10.48550/arXiv.2010.11929 [Google Scholar]
- M. Ester, H.-P. Kriegel, J. Sander, X. Xu, A density-based algorithm for discovering clusters in large spatial databases with noise, in Proceedings of the Second International Conference on Knowledge Discovery and Data Mining (KDD-96), Portland, OR, USA, August 2-4 (1996), 226-231 [Google Scholar]
- M. Mieskolainen, "HyperTrack: Neural Combinatorics for High Energy Physics, " Proceedings of CHEP 2023, arXiv:2309.14113 2023. doi: https://doi.org/10.48550/arXiv.2309.14113 [Google Scholar]
Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.
Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.
Initial download of the metrics may take a while.

