Optimization framework for a radio frequency gun based injector

Linear accelerator based light sources are used to produce coherent x-ray beams with unprecedented peak intensity. In these devices, the key parameters of the photon beam such as brilliance and coherence are directly dependent on the electron beam parameters. This leads to stringent beam quality requirements for the electron beam source. Radio frequency (RF) guns are used in such light sources since they accelerate electrons to relativistic energies over a very short distance, thus minimizing the beam quality degradation due to space charge effects within the particle bunch. Designing such sources including optimization of its beam parameters is a complex process where one needs to meet many requirements simultaneously. It is useful to have a tool to automate the design optimization in the context of the injector beam dynamics performance. Evolutionary and genetic algorithms are powerful tools to apply to nonlinear multi-objective optimization problems, and they have been successfully used in injector optimizations where the electric field profiles for the accelerating devices are fixed. Here the genetic algorithm based approach is extended to modify and optimize the electric field profile for an RF gun concurrently with the injector performance. Two field modification methods are used. This dissertation presents an overview of the optimization system and examples of its application to a state of the art RF gun. Results indicate improved injector performance is possible with unbalanced electric field profiles where the peak field in the cathode cell is larger than in subsequent cells.