Feasibility Investigation on Several Reinforcement Learning Techniques to Improve the Performance of the FERMI Free-Electron Laser

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. The doctoral project fits into this scope, investigating the introduction of new methods to automatically improve the performance of a specific type of particle accelerators: seeded free-electron lasers. The optimization of such systems is a challenging task, already faced in years by many different approaches in order to find and attain an optimal working point, keeping it optimally tuned despite drift or disturbances. Despite the good results achieved, better ones are always sought for. For this reason, several methods belonging to reinforcement learning, an area of machine learning that is attracting more and more attention in the scientific field, have been applied on FERMI, the free-electron laser facility at Elettra Sincrotrone Trieste. The research activity has been carried out by applying both model-free and model-based techniques belonging to reinforcement learning. Satisfactory preliminary results have been obtained, that present the first step toward a new fully automatic procedure for the alignment of the seed laser to the electron beam. In the meantime, at the Conseil Européen pour la Recherche Nucléaire, CERN, a similar investigation was ongoing. In the last year of the doctoral course, a collaboration to share the knowledge on the topic took place. Some of the results collected on the largest particle physics laboratory in the world are presented in the doctoral dissertation.