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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

DOI
10.1103/physrevaccelbeams.27.084801
Journal article

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

DOI
10.1103/physrevaccelbeams.27.054601
Journal article

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

DOI
10.1007/978-3-031-65993-5_21
Chapter

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

DOI
10.1117/12.2657489
Conference Paper

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

DOI
10.1103/physrevaccelbeams.26.034601
Journal article

2022

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

DOI
10.1109/tns.2021.3084515
Journal article

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

DOI
10.1103/physrevaccelbeams.22.061001
Journal article

2018