Phd Thesis

T. Boltz Karlsruhe Insitute of Technology KIT PhD thesis Abstract At the time this thesis is written, the world finds itself amidst and partly in the process of recovering from the COVID-19 pandemic caused by the SARS-Cov-2 virus. One major contribution to the worldwide efforts of bringing this pandemic to an end are the vaccines developed by different research teams all around the globe. Produced in a remarkably short time frame, a crucial first step for the discovery of these vaccines was mapping out the atomic structure of the proteins making up the virus and their interactions. Due to the bright X-rays required in the process, synchrotron light sources play an active role in the ongoing efforts of accomplishing that goal. Synchrotron light sources are particle accelerators that are capable of providing intense electromagnetic radiation by accelerating packages of electrons, called bunches, and forcing them on curved trajectories. Besides the support of research on the SARS-Cov-2 virus, the remarkable properties of synchrotron radiation lead to a multitude of applications in a variety of scientific fields such as materials science, geology, biology and medicine. As a special form of synchrotron radiation, this thesis is concerned with the coherent synchrotron radiation (CSR) generated by short electron bunches in a storage ring. At wavelengths larger than the size of the emitting electron structure, the particles within a bunch radiate coherently. This coherent emission of synchrotron radiation scales with the number of involved particles and can thus enhance the intensity of the emitted radiation by several orders of magnitude. As a consequence, modern synchrotron light sources, such as the Karlsruhe Research Accelerator (KARA) at the Karlsruhe Institute of Technology (KIT), are deliberately operating with short bunch lengths to extend the radiated CSR spectrum to higher frequencies and to increase the intensity of the emitted radiation. Yet, the continuous reduction of the bunch length at high beam intensities eventually leads to complex longitudinal dynamics caused by the self-interaction of the electron bunches with their own emitted CSR. This phenomenon, generally referred to as micro-bunching or micro-wave instability, can lead to the formation of dynamically changing micro-structures within the charge distribution of the electron bunches and thus to a uctuating emission of CSR. Moreover, it can cause oscillations of the bunch length and the energy spread, which can be detrimental to the operation of a synchrotron light source. On the other hand, as electron structures smaller than the full electron bunch, the micro-structures created by the instability lead to an increased emission of CSR at frequencies up to the THz frequency range. The instability can thus also be beneficial for a variety of applications that rely on intense radiation in that particular frequency range. ...

L. Grech University of Malta PhD thesis Abstract The Large Hadron Collider (LHC) at the European Organization for Nuclear Research (CERN) is the largest synchrotron built to date, having a circumference of approx- imately 27km. The LHC is able to accelerate two counter-rotating proton and/or heavy-ion beams up to 7 TeV per charge. These highly energetic beams are contained inside a vacuum chamber with an inner diameter of 80 mm by means of strong mag- netic fields produced by superconducting magnets. A beam cleaning and machine protection system is in place to prevent high-energy halo particles from impacting and heating the superconducting magnets. ...

N. Bruchon University of Trieste PhD thesis Abstract The research carried out in particle accelerator facilities does not concern only particle and condensed matter physics, although these are the main topics covered in the field. Indeed, since a particle accelerator is composed of many different sub-systems, its proper functioning depends both on each of these parts and their interconnection. It follows that the study, implementation, and improvement of the various sub-systems are fundamental points of investigation too. In particular, an interesting aspect for the automation engineering community is the control of such systems that usually are complex, large, noise-affected, and non-linear. ...

A. L. Edelen Colorado State University PhD thesis Abstract Charged particle accelerators support a wide variety of scientific, industrial, and medical applications. They range in scale and complexity from systems with just a few components for beam acceleration and manipulation, to large scientific user facilities that span many kilometers and have hundreds-to-thousands of individually-controllable components. Specific operational requirements must be met by adjusting the many controllable variables of the accelerator. Meeting these requirements can be challenging, both in terms of the ability to achieve specific beam quality metrics in a reliable fashion and in terms of the time needed to set up and maintain the optimal operating conditions. One avenue toward addressing this challenge is to incorporate techniques from the fields of machine learning (ML) and artificial intelligence (AI) into the way particle accelerators are modeled and controlled. While many promising approaches within AI/ML could beused for particle accelerators, this dissertation focuses on approaches based on neural networks. Neural networks are particularly well-suited to modeling, control, and diagnostic analysis of non-linear systems, as well as systems with large parameter spaces. They are also very appealing for their ability to process high-dimensional data types, such as images and time series (both of which are ubiquitous in particle accelerators). In this work, key studies that demonstrated the potential utility of modern neural network-based approaches to modeling and control of particle accelerators are presented. The context for this work is important: at the start of this work in 2012, there was little interest in AI/ML in the particle accelerator community, and many of the advances in neural networks and deep learning that enabled its present success had not yet been made at that time. As such, this work was both an exploration of possible application areas and a generator of initial demonstrations in these areas, including some of the first applications of modern deep neural networks in particle accelerators. ...