Speaker
Description
Laser-plasma accelerators (LPAs) generate ultrashort high intensity electron bunches from a compact source size. At the Karlsruhe Institute of Technology (KIT), we will use an LPA as one of the injectors for the compact, high-momentum acceptance, non-equilibrium storage ring cSTART.
The LPA injector with a length of only a few millimeters will be optimized to match the cSTART operation beam energy of 40-90 MeV. It will be based on an ionization trapping scheme in combination with a tailored plasma density profile to produce an electron beam with small energy spread that maximizes the spectral charge density at our target energy, which is (for LPAs) comparably low. Moreover, the LPA injector must produce controlled electron beams with high shot-to-shot stability and avoid high-energy tails. These goals can be achieved largely by the detailed design of the plasma density profile and the laser pulse parameters.
In an LPA, small changes across the high-dimensional parameter space can have a disproportional influence on overall performance. To find parameters for stable high-quality LPA beams, we perform particle-in-cell (PIC) simulations and implement a machine-learning driven approach by using Bayesian Optimization (BO) based on Gaussian Process Regression (GPR). This procedure allows us to both optimize our gas target design and characterize the effects of the interaction parameters, giving us a functional LPA with a simple tuning mechanism.
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