用粒子模拟和进化算法优化Laser -等离子体相互作用加速离子
Optimizing Laser-Plasma Interactions for Ion Acceleration using Particle-in-Cell Simulations and Evolutionary Algorithms
摘要：
基于超强激光的高能离子源的发展是一个重要的目标，具有各种潜在的应用。实现这一目标的障碍之一是需要最大化从激光能量到离子能量的转换效率。我们应用一种新的方法来解决这个问题，在该方法中，我们使用进化算法来优化转换效率，通过探索目标密度分布的变化与成千上万的一维粒子在细胞（PIC）模拟。然后，我们比较这个“最佳”的目标确定的一维PIC模拟更传统的选择，如与指数尺度长度预等离子体，与全三维PIC模拟。优化的目标优于传统的目标，在最大离子能量的20%，并显示出显着提高转换效率的高能量离子。这种靶几何形状增强了激光与电子的耦合，同时仍然允许激光从有效薄的靶强烈反射。这些结果强调了这种几何驱动方法的潜力，以指导研究优化激光等离子体模拟和实验。
英文摘要：
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 significant enhancement of conversion efficiency to high energy 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.
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