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Machine Learning and generative modeling in particle physics analysis and simulation |
Contacts
To get in touch with the particles team, please contact Frank Gaede
Team Members
Related projects
Recent publications
- Neural-Network Extraction of Unpolarized Transverse-Momentum-Dependent Distributions: Alessandro Bacchetta et al., Phys. Rev. Lett., doi: 10.1103/csc2-bj91
- OmniJet-α_C: learning point cloud calorimeter simulations using generative transformers: Joschka Birk et al., J. Inst., doi: 10.1088/1748-0221/20/07/P07007
- CaloHadronic: a diffusion model for the generation of hadronic showers: Thorsten Buss et al., arXiv, doi: 10.48550/arXiv.2506.21720
- Gaussian process regression as a sustainable data-driven background estimate method at the (HL)-LHC: Jackson Barr et al., Eur. Phys. J. C, doi: 10.1140/epjc/s10052-025-14574-3
- Simulating matrix models with tensor networks: Enrico M. Brehm et al., J. High Energ. Phys., doi: 10.1007/JHEP09(2025)116