Machine Learning Approach to Temporal Pulse Shaping for the Photoinjector Laser at CLARA

The temporal profile of the electron bunch is of critical importance in accelerator areas such as free-electron lasers and novel acceleration. In FELs, it strongly influences factors including efficiency and the profile of the photon pulse generated for user experiments, while in novel acceleration techniques it contributes to enhanced interaction of the witness beam with the driving electric field. Work is in progress at the CLARA facility at Daresbury Laboratory on temporal shaping of the ultraviolet photoinjector laser, using a fused-silica acousto-optic modulator. Generating a user-defined (programmable) time-domain target profile requires finding the corresponding spectral phase configuration of the shaper; this is a non-trivial problem for complex pulse shapes. Physically informed machine learning models have shown great promise in learning complex relationships in physical systems, and so we apply machine learning techniques here to learn the relationships between the spectral phase and the target temporal intensity profiles. Our machine learning model extends the range of available photoinjector laser pulse shapes by allowing users to achieve physically realisable configurations for arbitrary temporal pulse shapes.
Proceedings of the 13th International Particle Accelerator Conference, IPAC2022, Bangkok, Thailand