Sprecher
Beschreibung
Laser-plasma acceleration (LPA) aims to accelerate particles by exploiting the large electric field that can be achieved in a plasma. This field exceeds its counterparts in the rf-linacs and thus promises compact alternatives for the conventional accelerators.
The LPA process is highly non-linear and depends on a large number of laser and plasma parameters that make its optimization challenging. To be able to exploit the full potential of these accelerators, we need to use machine learning. In this field, Bayesian optimization is well suited to find the optimum of Particle-In-Cell (PIC) simulations, the main modeling technique for LPA.
In this presentation I will describe the properties of a LPA system in which we look for tuning and for which we currently consider the density profile of the plasma rather than the properties of the laser. We used the Multi-Objective Bayesian Optimization (MOBO) approach in which we are looking for the balance between energy spread and mean energy. This allows us to draw the Pareto Front which characterizes the best compromise achievable by our system.
