三角形纳米结构靶对质子加速的适应性和鲁棒性
Suitability and robustness of triangular nanostructured targets for proton acceleration
摘要：
MeV范围内的离子加速可以通过台式激光技术常规实现。当前的挑战之一是在不增加激光峰值功率的情况下改善从激光到质子束的能量耦合。在前靶表面引入纳米结构被证明有利于能量向电子的有效转移。在这篇手稿中，我们通过使用全尺寸的三维粒子模拟和有限的激光脉冲来研究，当使用具有三角形纳米结构（以前发现允许97%的激光能量吸收）的建议的最佳目标时的过程。我们表明，吸收的激光能量不依赖于所提出的参数范围内的维数。我们还提出了一个激光吸收的分析模型，包括偏离理想条件。这是支持的数值参数研究，建立相对于纳米结构的尺寸，使用不同的离子种类，存在的preplasma等的公差，我们发现，改变目标厚度或使用不同的离子不影响吸收，但它确实影响不同的等离子体物种之间的能量重新分配。最佳配置（$h = 1~λ,~ w = 0.7~ λ$）相对于目标制造误差是稳健的。然而，需要高对比度的激光脉冲，因为在非最佳情况下，具有大约0.5 λ的厚度的预等离子体层足以将激光吸收降低超过10%。
英文摘要：
Ion acceleration in the MeV range can be routinely achieved with table-top laser technology. One of the current challenges is to improve the energy coupling from the laser to the proton beam without increasing the laser peak power. Introducing nanostructures at the front target surface was shown to be beneficial for an efficient transfer of energy to the electrons. In this manuscript, we study by using full-scale three-dimensional particle-in-cell simulations and finite laser pulses, the process when a proposed optimal target with triangular nanostructure (previously found to allow 97% laser energy absorption) is used . We demonstrate that the absorbed laser energy does not depend on the dimensionality in the range of parameters presented. We also present an analytical model for laser absorption that includes deviations from the ideal conditions. This is supported by a numerical parameter study that establishes the tolerance with respect to the nanostructure size, use of different ion species, existence of preplasma, etc. We found that altering the target thickness or using different ions does not affect the absorption, but it does affect the energy redistribution among the different plasma species. The optimal configuration ($h = 1~λ,~ w = 0.7~ λ$) is robust with respect to the target fabrication errors. However, high contrast laser pulses are required, because a pre-plasma layer with a thickness on the order of 0.5 lambda is enough to lower the laser absorption by more than a 10% in a non-optimal scenario.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
