论文标题
使用粒子中的粒子模拟和进化算法优化激光 - 血浆相互作用以进行离子加速度
Optimizing Laser-Plasma Interactions for Ion Acceleration using Particle-in-Cell Simulations and Evolutionary Algorithms
论文作者
论文摘要
具有各种潜在应用的高能源离子的基于超强激光的来源的发展是一个重要目标。实现这一目标的障碍之一是需要最大程度地提高激光能量到离子能的转化效率。我们将一种新方法应用于此问题,在该方法中,我们使用进化算法来通过探索目标密度谱的变化,并使用数千个一维粒子中的粒子(PIC)模拟来优化转化效率。然后,我们将一维PIC仿真确定的“最佳”目标与更常规的选择(例如,具有指数尺度长度的预制量)和完全三维的PIC模拟进行了比较。最佳目标以最大离子能量的表现优于常规目标,并显示出对高能离子的转化效率的显着提高。该靶标几何形状增强了激光耦合到电子,同时仍允许激光从有效薄的靶标中强烈反射。这些结果强调了这种统计驱动的方法的潜力,以指导研究优化激光 - 血浆模拟和实验。
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.