Optimization and control of synchrotron emission in ultraintense laser-solid interactions using machine learning
Optimization and control of synchrotron emission in ultraintense laser-solid interactions using machine learning
复制标题
使用机器学习优化和控制超强激光-固体相互作用中的同步加速器发射
DOI：
10.1017/hpl.2023.11
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发表时间：
2023
影响因子：
4.8
通讯作者：
Goodman J
中科院分区：
文献类型：
--
作者：
Goodman J
利用贝叶斯优化方法，通过高斯过程回归，对二维粒子模拟进行了研究，得到了在超强激光脉冲与平面薄膜相互作用中产生同步辐射的最佳参数。同步辐射的个别属性，如产量，被最大化，并实现了多变量目标函数的韧致辐射的同时缓解。激光脉冲入射到靶上的角度被示出强烈地影响同步辐射产率和角分布，斜入射产生最佳结果。这在3D模拟中进一步探索，其中通过改变激光的偏振来证明同步辐射的空间分布的额外控制。结果表明，应用基于机器学习的优化方法的实用性，并提供新的见解辐射产生的物理激光箔相互作用，这将通知量子电动力学（QED）等离子体制度的实验设计。
The optimum parameters for the generation of synchrotron radiation in ultraintense laser pulse interactions with planar foils are investigated with the application of Bayesian optimization, via Gaussian process regression, to 2D particle-in-cell simulations. Individual properties of the synchrotron emission, such as the yield, are maximized, and simultaneous mitigation of bremsstrahlung emission is achieved with multi-variate objective functions. The angle-of-incidence of the laser pulse onto the target is shown to strongly influence the synchrotron yield and angular profile, with oblique incidence producing the optimal results. This is further explored in 3D simulations, in which additional control of the spatial profile of synchrotron emission is demonstrated by varying the polarization of the laser light. The results demonstrate the utility of applying a machine learning-based optimization approach and provide new insights into the physics of radiation generation in laser–foil interactions, which will inform the design of experiments in the quantum electrodynamics (QED)-plasma regime.