Machine Learning for optimization of design and automated system control of particle accelerator

Contacts

To get in touch with the accelerator team, please contact Ilya Agapov or Annika Eichler

Team members

  1. Ilya Agapov
  2. Lynda Boukela
  3. Annika Eichler
  4. Christian Grech
  5. Arne Grünhagen
  6. Nur Zulaiha Jomhari
  7. Jan Kaiser
  8. Raimund Kammering
  9. Maximilian Schütte
  10. Oliver Stein
  11. Sergey Tomin
  12. Bianca Veglia

Related projects

Recent publications

  1. Bridging the gap between machine learning and particle accelerator physics with high-speed, differentiable simulations: Jan Kaiser et al., Phys. Rev. Accel. Beams, doi: 10.1103/PhysRevAccelBeams.27.054601
  2. Large Language Models for Human-Machine Collaborative Particle Accelerator Tuning through Natural Language: Jan Kaiser et al., arXiv, doi: 10.48550/arXiv.2405.08888
  3. Learning to Do or Learning While Doing: Reinforcement Learning and Bayesian Optimisation for Online Continuous Tuning: Jan Kaiser et al., arXiv, doi: 10.48550/arXiv.2306.03739
  4. Predictive Maintenance for the Optical Synchronization System of the European XFEL: A Systematic Literature Survey: Arne Grünhagen et al., BTW2023 - Datenbanksysteme für Business, Technologie und Web, doi: 10.18420/BTW2023-70
  5. Anomaly detection at the European X-ray Free Electron Laser using a parity-space-based method: Annika Eichler et al., Physical Review Accelerators and Beams, doi: 10.1103/PhysRevAccelBeams.26.012801