5 papers · 1 filter
Five-Dimensional Beam Sigma Matrix Determination in Transport Lines with Differentiable Simulation
Chenran Xu, Louis Emery, Osama Mohsen +4
Precise measurement of the beam sigma matrix is essential for matching the optics in transport lines and ensuring reliable accelerator operation. In this work, we present a method…
The Memory Scaling of Reverse-Mode Differentiation in Particle Accelerator Simulations with Space Charge
Arjun Dhamrait, Edoardo Zoni, Axel Huebl +7
The recent development of differentiable simulation codes for particle accelerators has enabled gradient-based workflows that promise finer control and more realistic modeling of a…
Microsecond-Latency Feedback at a Particle Accelerator by Online Reinforcement Learning on Hardware
Luca Scomparin, Michele Caselle, Andrea Santamaria Garcia +11
The commissioning and operation of future large-scale scientific experiments will challenge current tuning and control methods. Reinforcement learning (RL) algorithms are a promisi…
Cheetah: Bridging the Gap Between Machine Learning and Particle Accelerator Physics with High-Speed, Differentiable Simulations
Jan Kaiser, Chenran Xu, Annika Eichler +1
Machine learning has emerged as a powerful solution to the modern challenges in accelerator physics. However, the limited availability of beam time, the computational cost of simul…
Bayesian Optimization Algorithms for Accelerator Physics
Ryan Roussel, Auralee L. Edelen, Tobias Boltz +23
Accelerator physics relies on numerical algorithms to solve optimization problems in online accelerator control and tasks such as experimental design and model calibration in simul…