High-fidelity physics simulations are powerful tools in the design and optimization of charged particle accelerators. However, the computational burden of these simulations often limits their use in practice for design optimization and experiment planning. It also precludes their use as on-line models tied directly to accelerator operation. We introduce an approach based on machine learning to create nonlinear, fast-executing surrogate models that are informed by a sparse sampling of the physics simulation. The models are $\mathcal{O}({10}^6)–\mathcal{O}({10}^7)$ times more computationally efficient to execute. We also demonstrate that these models can be reliably used with multiobjective optimization to obtain orders-of-magnitude speedup in initial design studies and experiment planning. For example, we required 132 times fewer simulation evaluations to obtain an equivalent solution for our main test case, and initial studies suggest that between 330–550 times fewer simulation evaluations are needed when using an iterative retraining process. Our approach enables new ways for high-fidelity particle accelerator simulations to be used, at comparatively little computational cost.

This paper introduces an experimental probe of the sterile neutrino with a novel, high-intensity source of electron antineutrinos from the production and subsequent decay of ${}^8\mathrm{Li}$. When paired with an existing $\sim 1$ kton scintillator-based detector, this $⟨E_{\nu }⟩=6.4$ MeV source opens a wide range of possible searches for beyond standard model physics via studies of the inverse beta decay interaction ${\overline{\nu }}_e+p\to e^{+}+n$. In particular, the experimental design described here has unprecedented sensitivity to ${\overline{\nu }}_e$ disappearance at $\Delta m^2\sim 1{\mathrm{eV}}^2$ and features the ability to distinguish between the existence of zero, one, and two sterile neutrinos.

Space-charge effects, being one of the most significant collective effects, play an important role in high intensity cyclotrons. However, for cyclotrons with small turn separation, other existing effects are of equal importance. Interactions of radially neighboring bunches are also present, but their combined effects have not yet been investigated in any great detail. In this paper, a new particle in the cell-based self-consistent numerical simulation model is presented for the first time. The model covers neighboring bunch effects and is implemented in the three-dimensional object-oriented parallel code OPAL-cycl, a flavor of the OPAL framework. We discuss this model together with its implementation and validation. Simulation results are presented from the PSI 590~MeV ring cyclotron in the context of the ongoing high intensity upgrade program, which aims to provide a beam power of 1.8~MW (CW) at the target destination.

Using a cyclotron-based model problem, we demonstrate for the first time the applicability and usefulness of an uncertainty quantification (UQ) approach in order to construct surrogate models. The surrogate model quantities, for example, emittance, energy spread, or the halo parameter, can be used to construct a global sensitivity model along with error propagation and error analysis. The model problem is chosen such that it represents a template for general high-intensity particle accelerator modeling tasks. The usefulness and applicability of the presented UQ approach is then demonstrated on an ongoing research project, aiming at the design of a compact high-intensity cyclotron. The proposed UQ approach is based on polynomial chaos expansions and relies on a well-defined number of high-fidelity particle accelerator simulations. Important uncertainty sources are identified using Sobol' indices within the global sensitivity analysis.

Modern synchrotron light source storage rings, such as the Swiss Light Source upgrade (SLS 2.0), use multibend achromats in their arc segments to achieve unprecedented brilliance. This performance comes at the cost of increased focusing requirements, which in turn require stronger sextupole and higher-order multipole fields for compensation of their effects on particles with energy deviation and lead to a considerable decrease in the dynamic aperture and/or energy acceptance. In this paper, to increase these two quantities, a multiobjective genetic algorithm (MOGA) is combined with a modified version of the well-known tracking code tracy. As a first approach, a massively parallel implementation of a MOGA is used. Compared to a manually obtained solution this approach yields very good results. However, it requires a long computation time. As a second approach, a surrogate model based on artificial neural networks is used in the optimization. This improves the computation time, but the quality of the results deteriorates beyond that of the manually obtained solution. As a third approach, the surrogate model is retrained during the optimization. This ensures a solution quality comparable to the one obtained with the first approach while also providing an order of magnitude speedup. Finally, good candidate solutions for SLS 2.0 are shown and further analyzed.