Speaker
Description
Dielectric-lined waveguides (DLWs) can support hybrid modes with strong accelerating longitudinal and transverse focusing electric-field components. By precisely tailoring the waveguide geometry, the electromagnetic field profile, phase velocity, and group velocity can be tuned to maintain synchronisation between relativistic bunches and the accelerating mode over extended distances. When driven by terahertz (THz) frequency pulses, DLWs can deliver high accelerating gradients over centimetre-scale interaction lengths.
In recent years, machine learning techniques have been successfully used to optimise and control particle accelerators. Here, we employ these techniques to identify beamline designs that simultaneously accelerate and focus electron beams. Specifically, a reinforcement-learning approach is used to optimise the DLW layout through a reward function that encourages preservation of the beam Twiss parameters and overall bunch quality.
Beam transport and acceleration are simulated using analytic models of the DLW accelerating modes in two distinct regimes: a weakly relativistic regime, where space charge dominates the transverse dynamics, and an ultrarelativistic regime, starting from a 30 MeV electron beam. This simulation study provides a foundation for future multi-stage THz-driven DLW acceleration experiments.
| Presenting Author | Filip Peczek |
|---|---|
| Is the Presenting Author a PhD Student or Early Career Scientist ? | Yes |
| Area of research | Advanced accelerator concepts |