Turn-key constrained parameter space exploration for particle accelerators using Bayesian active learning
Accelerator operators often need to characterize the beam response with respect to a set of input parameters. This process is made difficult since the beam response can have varying sensitivities to individual input parameters. Furthermore, practical constraints often make measuring beam parameters difficult or impossible. As a result, simple parametric scans used to perform this characterization task are inefficient to entirely impractical. Our work demonstrates a novel algorithm that autonomously characterizes the beam response in an efficient manner, with limited prior knowledge of measurement sensitivities, while navigating a highly constrained measurement space.
This work contributes towards the goals of Theme 3: Beam Dynamics and Control which aims to develop novel methods for improving control of high brightness accelerators. Using these advanced control methods, CBB enables improved transport of beams once they are generated to free electron laser and collider applications.
This advancement solves an extremely common experimental or computational problem faced by those in the accelerator, physical, chemical and biological scientific fields. As such, it is applicable towards experimental design in many different situations, especially novel or poorly understood contexts. Scientists in the physics community will be able to greatly improve the efficiency and success of their experiments using this work.
R. Roussel, J. P. Gonzalez-Aguilera, Y.-K. Kim, E. Wisniewski, W. Liu, P. Piot, J. Power, A. Hanuka, and A. Edelen, “Turn-Key Constrained Parameter Space Exploration for Particle Accelerators Using Bayesian Active Learning,” Nature Communications, vol. 12, p. 5612, Sep. 2021, doi: 10.1038/s41467-021-25757-3