Optimizing laser–plasma interactions for ion acceleration using particle-in-cell simulations and evolutionary algorithms

Abstract

The development of ultra-intense laser-based sources of high energy ions is an important goal, with a variety of potential applications. One of the barriers to achieving this goal is the need to maximize the conversion efficiency from laser energy to ion energy. We apply a new approach to this problem, in which we use an evolutionary algorithm to optimize conversion efficiency by exploring variations of the target density profile with thousands of one-dimensional particle-in-cell (PIC) simulations. We then compare this ‘optimal’ target identified by the one-dimensional PIC simulations to more conventional choices, such as with an exponential scale length pre-plasma, with fully three-dimensional PIC simulations. The optimal target outperforms the conventional targets in terms of maximum ion energy by 20% and show a noticeable enhancement of conversion efficiency to >2 MeV ions. This target geometry enhances laser coupling to the electrons, while still allowing the laser to strongly reflect from an effectively thin target. These results underscore the potential for this statistics-driven approach to guide research into optimizing laser–plasma simulations and experiments.

Unsupervised discovery of nonlinear plasma physics using differentiable kinetic simulations

“…where f MX (n 0 , v th0 ) = n 0 exp(−v 2 /v 2 th )/ πv 2 th . By having non-zero ν K only in a small localized region at the boundary, as also described by Strozzi et al (2007), and as shown in figure 8, the particles are absorbed in a fixed Maxwellian, similar to having a thermal boundary condition. This localization is also performed using the hyperbolic tangent parameterization from (A13) but using 1 − g( p, x).…”

“…These nonlinear electrostatic wavepackets are dynamically evolving, finite-length analogues of the well-known, time-independent, periodic Bernstein-Greene-Kruskal (BGK) modes described in Bernstein et al (1957). Simulations of stimulated Raman scattering (SRS) in inertial confinement fusion scenarios show that similar large-amplitude waves, but of finite extent, are generated in the laser-plasma interaction, and that particle trapping is correlated with the transition to the high-reflectivity burst regime of SRS (Strozzi et al 2007;Ellis et al 2012).…”

“…Numerical solutions to the 1D-1V VPFP equation set have been applied in research on laser-plasma interactions in the context of inertial fusion, for example in plasma-based accelerators (Krall, Joyce & Esarey 1991;Thomas 2016), space physics (Chen, Klein & Howes 2019), fundamental plasma physics (Pezzi et al 2019) and inertial fusion (Strozzi et al 2007;Fahlen et al 2009;Banks et al 2011). Such numerical simulations may be used to explore initial conditions and forcing functions to understand the behaviour of a physical effect in response to input parameters.…”

“…For example, when the objective function is the outcome of a plasma-based accelerator experiment. In this case, one can use classical approaches like the Nelder-Mead algorithm (Shalloo et al 2020) or more modern alternatives like Bayesian (Shalloo et al 2020;Jalas et al 2021) or evolutionary (He et al 2015;Smith et al 2020) algorithms. These algorithms have been shown to be effective in the small N p regime, where N p is the number of parameters to optimize.…”

Multi-parameter Bayesian optimisation of laser-driven ion acceleration in particle-in-cell simulations

“…By contrast, ML approaches based on neural networks and genetic algorithms require large quantities of data to make accurate predictions, and direct optimisation algorithms can struggle to optimise noisy functions. Bayesian optimisation has been applied to laser-driven electron acceleration experiments [33][34][35], and Bayesian statistics have been used to model laser-driven ion acceleration studies [36], along with other common ML techniques [37,38].…”