arXiv subjects

Tobias Boltz

Publications and source records attributed to Tobias Boltz.

6 recordsLinked to original sources

arXiv2025

Detuning Properties of RF Phase Modulation in an Electron Storage Ring

In electron storage rings, it is possible to increase the bunch length by applying phase modulation to the radio frequency accelerating field by choosing appropriate parameters for the modulation. Such a bunch lengthening effect improves beam parameters such as beam lifetime, which can help us achieve better beam stability. It is well known that the modulation frequency around the double synchrotron frequency is effective in lengthening the bunch. The dependence of bunch lengthening on modulation frequency, the so-called detuning property, has a peak around the double synchrotron frequency with a frequency width that depends on the modulation amplitude. Nonlinear effects due to phase modulation lead to a peak frequency shift in a negative direction from the double synchrotron frequency, accompanied by an asymmetric peak shape. Beam current also affects the properties of the detuning condition of such bunch lengthening. We investigated the detuning property using a theoretical model and systematic measurements at the electron storage ring KARA, and we verified the qualitative agreement between the experiments and the theoretical model. The macro-particle simulations have confirmed the agreement.

physics.acc-ph↗

arXiv2025

Leveraging Prior Mean Models for Faster Bayesian Optimization of Particle Accelerators

Tuning particle accelerators is a challenging and time-consuming task that can be automated and carried out efficiently using suitable optimization algorithms, such as model-based Bayesian optimization techniques. One of the major advantages of Bayesian algorithms is the ability to incorporate prior information about beam physics and historical behavior into the model used to make control decisions. In this work, we examine incorporating prior accelerator physics information into Bayesian optimization algorithms by utilizing fast executing, neural network models trained on simulated or historical datasets as prior mean functions in Gaussian process models. We show that in ideal cases, this technique substantially increases convergence speed to optimal solutions in high-dimensional tuning parameter spaces. Additionally, we demonstrate that even in non-ideal cases, where prior models of beam dynamics do not exactly match experimental conditions, the use of this technique can still enhance convergence speed. Finally, we demonstrate how these methods can be used to improve optimization in practical applications, such as transferring information gained from beam dynamics simulations to online control of the LCLS injector, and transferring knowledge gained from experimental measurements across different operating modes, such as accelerating different ion species at the ATLAS heavy ion accelerator.

physics.acc-ph↗

arXiv2024

Bayesian Optimization Algorithms for Accelerator Physics

Accelerator physics relies on numerical algorithms to solve optimization problems in online accelerator control and tasks such as experimental design and model calibration in simulations. The effectiveness of optimization algorithms in discovering ideal solutions for complex challenges with limited resources often determines the problem complexity these methods can address. The accelerator physics community has recognized the advantages of Bayesian optimization algorithms, which leverage statistical surrogate models of objective functions to effectively address complex optimization challenges, especially in the presence of noise during accelerator operation and in resource-intensive physics simulations. In this review article, we offer a conceptual overview of applying Bayesian optimization techniques towards solving optimization problems in accelerator physics. We begin by providing a straightforward explanation of the essential components that make up Bayesian optimization techniques. We then give an overview of current and previous work applying and modifying these techniques to solve accelerator physics challenges. Finally, we explore practical implementation strategies for Bayesian optimization algorithms to maximize their performance, enabling users to effectively address complex optimization challenges in real-time beam control and accelerator design.

physics.acc-ph↗

arXiv2023

Bayesian Optimization of the Beam Injection Process into a Storage Ring

We have evaluated the data-efficient Bayesian optimization method for the specific task of injection tuning in a circular accelerator. In this paper, we describe the implementation of this method at the Karlsruhe Research Accelerator with up to nine tuning parameters, including the determination of the associated hyperparameters. We show that the Bayesian optimization method outperforms manual tuning and the commonly used Nelder-Mead optimization algorithm both in simulation and experiment. The algorithm was also successfully used to ease the commissioning phase after the installation of new injection magnets and is regularly used during accelerator operations. We demonstrate that the introduction of context variables that include intra-bunch scattering effects, such as the Touschek effect, further improves the control and robustness of the injection process.

physics.acc-ph↗

arXiv2022

On the Perturbation of Synchrotron Motion in the Micro-Bunching Instability

The self-interaction of short electron bunches with their own radiation field can have a significant impact on the longitudinal beam dynamics in a storage ring. While higher bunch currents increase the power of the emitted CSR which can be provided to dedicated experiments, it simultaneously amplifies the strength of the self-interaction. Eventually, this leads to the formation of dynamically changing micro-structures within the bunch and thus fluctuating CSR emission, a phenomenon that is generally known as micro-bunching or micro-wave instability. The underlying longitudinal dynamics can be simulated by solving the VFP equation, where the CSR self-interaction can be added as a perturbation to the Hamiltonian. In this contribution, we focus on the perturbation of the synchrotron motion that is caused by introducing this additional wake field. Therefore, we adopt the perspective of a single particle and eventually comment on its implications for collective motion. We explicitly show how the shape of the parallel plates CSR wake potential breaks homogeneity in the longitudinal phase space and propose a quadrupole-like mode as potential seeding mechanism of the micro-bunching instability. Moreover, we consider synchrotron motion above the instability threshold and thereby motivate an approach to control of the occurring micro-bunching dynamics. Using dynamically adjusted RF amplitude modulations we can directly address the continuous CSR-induced perturbation at the timescale of its occurrence, which allows for substantial control over the longitudinal charge distribution. While the approach is not limited to this particular application, we demonstrate how this can significantly mitigate the micro-bunching dynamics directly above the instability threshold. The gained insights are supported and verified using the VFP solver Inovesa and put into context with measurements at KARA.

physics.acc-ph↗

arXiv2018

Continuous bunch-by-bunch spectroscopic investigation of the micro-bunching instability

Electron accelerators and synchrotrons can be operated to provide short emission pulses due to longitudinally compressed or sub-structured electron bunches. Above a threshold current, the high charge density leads to the micro-bunching instability and the formation of sub-structures on the bunch shape. These time-varying sub-structures on bunches of picoseconds-long duration lead to bursts of coherent synchrotron radiation in the terahertz frequency range. Therefore, the spectral information in this range contains valuable information about the bunch length, shape and sub-structures. Based on the KAPTURE readout system, a 4-channel single-shot THz spectrometer capable of recording 500 million spectra per second and streaming readout is presented. First measurements of time-resolved spectra are compared to simulation results of the Inovesa Vlasov-Fokker-Planck solver. The presented results lead to a better understanding of the bursting dynamics especially above the micro-bunching instability threshold.

physics.acc-ph↗