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. Measurement of off-shell Higgs boson production in the H*->ZZ->4l decay channel using a neural simulation-based inference technique in 13 TeV pp collisions with the ATLAS detector: ATLAS collaboration et al., Rep. Prog. Phys., doi: 10.1088/1361-6633/adcd9a
  2. Neural-Network Extraction of Unpolarized Transverse-Momentum-Dependent Distributions: Alessandro Bacchetta et al., Phys. Rev. Lett., doi: 10.1103/csc2-bj91
  3. 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
  4. CaloHadronic: a diffusion model for the generation of hadronic showers: Thorsten Buss et al., arXiv, doi: 10.48550/arXiv.2506.21720
  5. 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