Articles citing this article

The Citing articles tool gives a list of articles citing the current article.
The citing articles come from EDP Sciences database, as well as other publishers participating in CrossRef Cited-by Linking Program. You can set up your personal account to receive an email alert each time this article is cited by a new article (see the menu on the right-hand side of the abstract page).

Cited article:

Navigation, field integration and track parameter transport through detectors using GPUs and CPUs within the ACTS R&D project

A Salzburger, J Niermann, B Yeo, A Krasznahorkay and S N Swatman
Journal of Physics: Conference Series 3206 (1) 012076 (2026)
https://doi.org/10.1088/1742-6596/3206/1/012076

Hits to Higgs: hit-level Higgs classification from raw LHC detector data using Higgsformer

Sascha Caron, Polina Moskvitina, Roberto Ruiz de Austri and Eugene Shalugin
The European Physical Journal C 86 (7) (2026)
https://doi.org/10.1140/epjc/s10052-026-15930-7

Learning to reconstruct quirky tracks

Qiyu Sha, Daniel Murnane, Max Fieg, Shelley Tong, Mark Zakharyan, Yaquan Fang and Daniel Whiteson
Physical Review D 114 (1) (2026)
https://doi.org/10.1103/dvzc-lgj1

ARGUS: The AI Eye for CERN - Solving Extreme Computational Physics

Nihal Gazi, Aditya K. Biswas, Aihik Basu, Anirban Chattopadhyay and Sayan Pratihar
EPJ Web of Conferences 370 01032 (2026)
https://doi.org/10.1051/epjconf/202637001032

A roadmap toward scaling, reasoning and self-evolving foundation models for nuclear and particle physics

Yihui Ren, Joseph D. Osborn, Elizabeth Brost, Haiwang Yu, Syed Haider Abidi, Yeonju Go, Shuhang Li, David Park, Yi Huang, Xihaier Luo, Yuewei Lin, Peter Boyle, Jin Huang, Alexei Klimentov, Michael Begel, Xin Qian, Torre J. Wenaus, Dmitri Denisov, Abhay Deshpande, Meifeng Lin, Shinjae Yoo, Viviana Cavaliere, James C. Dunlop and Hong Ma
International Journal of Modern Physics A 41 (15n16) (2026)
https://doi.org/10.1142/S0217751X26500867

Hierarchical Graph Neural Networks for Particle Track Reconstruction

Ryan Liu, Paolo Calafiura, Steven Farrell, Xiangyang Ju, Daniel Thomas Murnane and Tuan Minh Pham
Journal of Physics: Conference Series 3206 (1) 012084 (2026)
https://doi.org/10.1088/1742-6596/3206/1/012084

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

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

Sascha Caron, Nadezhda Dobreva, Antonio Ferrer Sánchez, José D. Martín-Guerrero, Uraz Odyurt, Roberto Ruiz de Austri Bazan, Zef Wolffs and Yue Zhao
The European Physical Journal C 85 (4) (2025)
https://doi.org/10.1140/epjc/s10052-025-14156-3

Prospects for novel track reconstruction algorithms based on Graph Neural Network models using telescope detector testbed

Wojciech Gomułka, Tomasz Szumlak, Piotr A. Kowalski, Tomasz Bołd, T. Szumlak, B. Rachwał, A. Dziurda, M. Schulz, D. vom Bruch, K. Ellis and S. Hageboeck
EPJ Web of Conferences 337 01262 (2025)
https://doi.org/10.1051/epjconf/202533701262

Quantum Machine Learning for Track Reconstruction

Laura Cappelli, Matteo Argenton, Concezio Bozzi, Enrico Calore, Sebastiano Fabio Schifano, T. Szumlak, B. Rachwał, A. Dziurda, M. Schulz, D. vom Bruch, K. Ellis and S. Hageboeck
EPJ Web of Conferences 337 01246 (2025)
https://doi.org/10.1051/epjconf/202533701246

From Hope to Heuristic: Realistic Runtime Estimates for Quantum Optimisation in NHEP

Maja Franz, Manuel Schönberger, Melvin Strobl, Eileen Kühn, Achim Streit, Pía Zurita, Markus Diefenthaler, Wolfgang Mauerer, T. Szumlak, B. Rachwał, A. Dziurda, M. Schulz, D. vom Bruch, K. Ellis and S. Hageboeck
EPJ Web of Conferences 337 01282 (2025)
https://doi.org/10.1051/epjconf/202533701282

Deep Learning Methods in High Luminosity Track Reconstruction Scenario: Applying TrackNET to TrackML Challenge

D. I. Rusov, P. V. Goncharov, G. A. Ososkov and A. S. Zhemchugov
Physics of Particles and Nuclei 56 (6) 1599 (2025)
https://doi.org/10.1134/S1063779625700935

Deep Learning Methods as a Tool for Overcoming the Crisis of Particle Tracking in High Luminosity HEP Experiments

G. A. Ososkov
Physics of Particles and Nuclei 56 (6) 1299 (2025)
https://doi.org/10.1134/S106377962570039X

Transformers for Charged Particle Track Reconstruction in High-Energy Physics

Samuel Van Stroud, Philippa Duckett, Max Hart, Nikita Pond, Sébastien Rettie, Gabriel Facini and Tim Scanlon
Physical Review X 15 (4) (2025)
https://doi.org/10.1103/md46-yqgd

Track reconstruction for the ATLAS Phase-II Event Filter using GNNs on FPGAs

Sebastian Dittmeier, R. De Vita, X. Espinal, P. Laycock and O. Shadura
EPJ Web of Conferences 295 02032 (2024)
https://doi.org/10.1051/epjconf/202429502032

Towards a realistic track reconstruction algorithm based on graph neural networks for the HL-LHC

Catherine Biscarat, Sylvain Caillou, Charline Rougier, et al.
EPJ Web of Conferences 251 03047 (2021)
https://doi.org/10.1051/epjconf/202125103047