Parallel general purpose multiobjective optimization framework with application to electron beam dynamics

Abstract

Particle accelerators are invaluable tools for research in the basic and applied sciences, such as materials science, chemistry, the biosciences, particle physics, nuclear physics and medicine. The design, commissioning, and operation of accelerator facilities is a non-trivial task, due to the large number of control parameters and the complex interplay of several conflicting design goals. The Argonne Wakefield Accelerator facility has some unique challenges resulting from its purpose to carry out advanced accelerator R&D. Individual experiments often have challenging beam requirements, and the physical configuration of the beamlines is often changed to accommodate the variety of supported experiments. The need for rapid deployment of different operational settings further complicates the optimization work that must be done for multiple constraints and challenging operational regimes. One example of this is an independently staged two-beam acceleration experiment which requires the construction of an additional beamline (this is now in progress). The high charge drive beam, well into the space charge regime, must be threaded through small aperture (17.6 mm) decelerating structures. In addition, the bunch length must be sufficiently short to maximize power generation in the decelerator. We propose to tackle this problem by means of multi-objective optimization algorithms which also facilitate a parallel deployment. In order to compute solutions in a meaningful time frame, a fast and scalable software framework is required. In this paper, we present a general-purpose framework for simulation-based multi-objective optimization methods that allows the automatic investigation of optimal sets of machine parameters. Using evolutionary algorithms as the optimizer and OPAL as the forward solver, validation experiments and results of multi-objective optimization problems in the domain of beam dynamics are presented. Optimized solutions for the new high charge drive beamline found by the framework were used to finish the design of a two beam acceleration experiment. The selected solution along with the associated beam parameters is presented.

Machine learning for orders of magnitude speedup in multiobjective optimization of particle accelerator systems

“…1.4.2 of the OPAL manual [31]. The specific values used in this case were chosen based on previous optimization work for the AWA that involved a hyperparameter scan [5]. The constraints of the problem were set such that the variables stayed within the operating ranges of the AWA (see Table I).…”

“…Optimization studies are important in the initial design of particle accelerator systems, when many trade-offs between possible setting combinations have to be explored. In practice, multiobjective optimization with genetic algorithms (GAs) [1,2] is frequently used for finding optimal setting combinations (see [3][4][5][6][7] for accelerator-specific examples). One advantage of using multiobjective optimization is that it enables one to examine optimal trade-offs between achievable beam parameters.…”

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

“…To find good operation points for the accelerators, we solved multi-objective optimisation problems [5,6,9,17]. The standard approach for that is visualized in Figure 3a: A physics based model, such as OPAL, together with a GA and random initialization is used.…”

“…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.…”

Online Multi-Objective Particle Accelerator Optimization of the AWAKE Electron Beam Line for Simultaneous Emittance and Orbit Control

“…For example, on a photocathode a trade off must be made between transverse emittance and bunch length because shorter bunches have higher charge density and therefore experience higher space charge forces that cause divergence 1 . Recently, multi-objective optimization approaches have been studied in simulation for RF cavity shape optimization to maximize shunt impedance while minimizing peak surface electric field 2 , for electron beam dynamics simulations of the Argonne Wakefield Accelerator Facility (AWA) 3 , and for 3D beam tracking in electrostatic beamlines 4 .…”