Speaker
Description
The 40MeV linear accelerator and 3$\mu$m free-electron laser (FEL) facility at UH offers a versatile platform for advanced beam physics experiments and compact radiation source development. In this talk, I will discuss opportunities for applying AI and machine learning methods to optimize key beam parameters critical for the performance of the FEL and the inverse Compton scattering (ICS). Targets include minimizing spot size and divergence at the ICS interaction point, controlling the beam energy spread, and maximizing beam current while avoiding beam loading and cathode back-heating. These optimization tasks involve tuning a high-dimensional parameter space—transport magnets, RF and gun settings, and cathode conditions—based on diagnostics such as beam position monitors, wire scanners, spectrometers, and others, some of which are not yet fully integrated into the control system.
