Publications
2024
Bayesian optimization algorithms for accelerator physics
Roussel, R., Edelen, A. L., Boltz, T., Kennedy, D., Zhang, Z., Ji, F., . . . Neiswanger, W. (n.d.). Bayesian optimization algorithms for accelerator physics. Physical Review Accelerators and Beams, 27(8). doi:10.1103/physrevaccelbeams.27.084801
Reinforcement learning-trained optimisers and Bayesian optimisation for online particle accelerator tuning.
Kaiser, J., Xu, C., Eichler, A., Santamaria Garcia, A., Stein, O., Bründermann, E., . . . Schlarb, H. (2024). Reinforcement learning-trained optimisers and Bayesian optimisation for online particle accelerator tuning.. Scientific reports, 14(1), 15733. doi:10.1038/s41598-024-66263-y
Bridging the gap between machine learning and particle accelerator physics with high-speed, differentiable simulations
Kaiser, J., Xu, C., Eichler, A., & Santamaria Garcia, A. (n.d.). Bridging the gap between machine learning and particle accelerator physics with high-speed, differentiable simulations. Physical Review Accelerators and Beams, 27(5). doi:10.1103/physrevaccelbeams.27.054601
Deep Meta Reinforcement Learning for Rapid Adaptation In Linear Markov Decision Processes: Applications to CERN’s AWAKE Project
Hirlaender, S., Pochaba, S., Lukas, L., Garcia, A. S., Xu, C., Kaiser, J., . . . Kain, V. (2024). Deep Meta Reinforcement Learning for Rapid Adaptation In Linear Markov Decision Processes: Applications to CERN’s AWAKE Project. In Advances in Intelligent Systems and Computing (pp. 175-183). Springer Nature Switzerland. doi:10.1007/978-3-031-65993-5_21
2023
Advanced diagnostic detectors for rogue phenomena, single-shot applications
Caselle, M., Bielawski, S., Chilingaryan, S., Czwalinna, M. K., Dritschler, T., Kopmann, A., . . . Simon, F. (2023). Advanced diagnostic detectors for rogue phenomena, single-shot applications. In G. Herink, D. R. Solli, & S. Bielawski (Eds.), Real-time Measurements, Rogue Phenomena, and Single-Shot Applications VIII (pp. 17). SPIE. doi:10.1117/12.2657489
Bayesian optimization of the beam injection process into a storage ring
Xu, C., Boltz, T., Mochihashi, A., Santamaria Garcia, A., Schuh, M., & Müller, A. -S. (n.d.). Bayesian optimization of the beam injection process into a storage ring. Physical Review Accelerators and Beams, 26(3). doi:10.1103/physrevaccelbeams.26.034601
2021
Accelerated Deep Reinforcement Learning for Fast Feedback of Beam Dynamics at KARA
Wang, W., Caselle, M., Boltz, T., Blomley, E., Brosi, M., Dritschler, T., . . . Fang, Y. (2021). Accelerated Deep Reinforcement Learning for Fast Feedback of Beam Dynamics at KARA. IEEE Transactions on Nuclear Science, 68(8), 1794-1800. doi:10.1109/tns.2021.3084515
2019
Prediction of beam losses during crab cavity quenches at the high luminosity LHC
Apsimon, R., Burt, G., Dexter, A., Shipman, N., Castilla, A., Macpherson, A., . . . Appleby, R. B. (n.d.). Prediction of beam losses during crab cavity quenches at the high luminosity LHC. Physical Review Accelerators and Beams, 22(6). doi:10.1103/physrevaccelbeams.22.061001