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

  1. Frank Gaede

Related projects

Recent publications

  1. Neural-Network Extraction of Unpolarized Transverse-Momentum-Dependent Distributions: Alessandro Bacchetta et al., Phys. Rev. Lett., doi: 10.1103/csc2-bj91
  2. 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
  3. CaloHadronic: a diffusion model for the generation of hadronic showers: Thorsten Buss et al., arXiv, doi: 10.48550/arXiv.2506.21720
  4. 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
  5. Simulating matrix models with tensor networks: Enrico M. Brehm et al., J. High Energ. Phys., doi: 10.1007/JHEP09(2025)116