53 citations · 215 across the 15 of their papers we have counts for
6 papers · 1 filter
Applications of Differentiable Physics Simulations in Particle Accelerator Modeling
Ryan Roussel, Auralee Edelen
Current physics models used to interpret experimental measurements of particle beams require either simplifying assumptions to be made in order to ensure analytical tractability, o…
Neural Network Prior Mean for Particle Accelerator Injector Tuning
Connie Xu, Ryan Roussel, Auralee Edelen
Bayesian optimization has been shown to be a powerful tool for solving black box problems during online accelerator optimization. The major advantage of Bayesian based optimization…
Beam shaping using an ultra-high vacuum multileaf collimator and emittance exchange beamline
N. Majernik, G. Andonian, W. Lynn +9
We report the development of a multileaf collimator (MLC) for charged particle beams, based on independently actuated tungsten strips which can selectively scatter unwanted particl…
Phase Space Reconstruction from Accelerator Beam Measurements Using Neural Networks and Differentiable Simulations
Ryan Roussel, Auralee Edelen, Christopher Mayes +5
Characterizing the phase space distribution of particle beams in accelerators is a central part of accelerator understanding and performance optimization. However, conventional rec…
Snowmass21 Accelerator Modeling Community White Paper
S. Biedron, L. Brouwer, D. L. Bruhwiler +24
After a summary of relevant comments and recommendations from various reports over the last ten years, this paper examines the modeling needs in accelerator physics, from the model…
Differentiable Preisach Modeling for Characterization and Optimization of Accelerator Systems with Hysteresis
R. Roussel, A. Edelen, D. Ratner +4
Future improvements in particle accelerator performance is predicated on increasingly accurate online modeling of accelerators. Hysteresis effects in magnetic, mechanical, and mate…