Matching of turn pattern measurements for cyclotrons using multiobjective optimization

Abstract

The usage of numerical models to study the evolution of particle beams is an essential step in the design process of particle accelerators. However, uncertainties of input quantities such as beam energy and magnetic field lead to simulation results that do not fully agree with measurements, hence the final machine will behave slightly differently than the simulations. In case of cyclotrons such discrepancies affect the overall turn pattern or may even alter the number of turns in the machine. Inaccuracies at the PSI Ring cyclotron facility that may harm the isochronism are compensated by additional magnetic fields provided by 18 trim coils. These are often absent from simulations or their implementation is very simplistic. In this paper a newly developed realistic trim coil model within the particle accelerator framework OPAL is presented that was used to match the turn pattern of the PSI Ring cyclotron. Due to the high-dimensional search space consisting of 48 design variables (simulation input parameters) and 182 objectives (i.e. turns) simulation and measurement cannot be matched in a straightforward manner. Instead, an evolutionary multi-objective optimization with a population size of more than 8000 individuals per generation together with a local search approach were applied that reduced the maximum absolute error to 4.5 mm over all 182 turns.

Cyclotron beam extraction by acceleration

“…The field map of the PSI Ring [6] that we used for the tracking is based on measured data [33,37,38] and has been used in several other recent publications, for instance refs. [39][40][41].…”

Global Sensitivity Analysis on Numerical Solver Parameters of Particle-In-Cell Models in Particle Accelerator Systems

“…In order to study the effect of AMR solver parameters and energy binning in neighbouring bunch simulations we perform sensitivity experiments with three different high intensity cyclotrons, the PSI Ring [55], the PSI Injector II [56] and the DAEδALUS Injector Cyclotron (DIC) [27,28]. We always accelerate 5 particle bunches with 10 6 particles.…”

Fast, Efficient and Flexible Particle Accelerator Optimisation Using Densely Connected and Invertible Neural Networks

“…For example, a single simulation of the Argonne Wakefield Accelerator (AWA) (Figure 1) takes approximately ten minutes with the high-fidelity physics model Object-Oriented Parallel Accelerator Library (OPAL) [3], which renders such models unsuitable for real-time usage. In order to achieve optimal operation points of these machines, genetic algorithms (GAs) are typically the method of choice for solving multi-objective optimisation problems [4][5][6][7][8]. However, an optimisation sometimes requires thousands of model evaluations.…”