Automated multi-dimensional mapping of dynamic laser-liquid interactions

高强度激光与物质的相互作用产生了具有非常高的温度和密度的极端环境，使得材料原子中的电子不再与原子核结合，材料成为等离子体。这些相互作用可以为研究包括超新星冲击和太阳耀斑在内的天体物理现象创造条件，并支持非常高的电场，这些电场可以用来加速带电粒子，距离比射频加速器技术的极限短100到1000秒。这些紧凑的加速器已经被证明可以产生具有非常理想的特性的离子束，用于材料测试、放射生物学和惯性聚变能源的关键应用。到目前为止，由于难以在数值和计算模型中再现这些相互作用的复杂行为，以及用于产生等离子体和驱动粒子加速的高能脉冲激光(通常为0.002赫兹-每10分钟一发)的低重复频率造成的现有数据有限，对这些相互作用的充分探索和利用一直受到阻碍。这在研究脆弱的超薄不透明目标时尤其如此，在这种情况下，激光对能量的吸收导致目标受热并膨胀，导致目标随着密度的下降而变得透明。当这种情况发生时，激光可以通过目标传播，激光能量向等离子体的转移不再局限于目标表面。这种相互作用很有意义，因为正是在这里，记录了最高能量的激光加速质子。新一代多赫兹高能激光技术正在推动数据采集率提高数量级。为了开发这些新的激光器，还有必要测试目标技术，以提供在多赫兹重复频率下具有高位置稳定性的新鲜超薄薄片。此外，尽管数据采集率大大提高，但相互作用动力学对大量变量(如激光能量、激光时空能量分布、目标密度分布)的依赖意味着，“网格扫描”每个参数不是绘制它们相互依赖关系的有效方法。通过结合机器学习工具，激光和靶标带来的高数据率可以被用来智能地对参数空间进行采样，以模拟相互作用并量化这些新型加速器的稳定性。拟议中的合作将通过将美国SLAC国家加速器实验室(SLAC)开发的液膜靶与由贝尔法斯特女王大学(QUB)的研究人员率先提出的计算机引导的激光等离子体实验新方法相结合来应对这一挑战。这一新型实验平台的开发将使人们能够更深入地了解激光与等离子体之间的关键能量传递途径及其对实验变量的依赖关系。这项研究将直接影响到等离子体建模、高级加速器研究、等离子体天体物理学、惯性约束聚变、材料测试和闪光放射生物学。研究成果将纳入EPSRC 2022-2025年的战略优先事项，重点是物理和数学科学强国、工程前沿和人工智能，通过以下研究主题提高技能：利用人工智能用于实验科学，为工程、卫生和政府提供人工智能和数据科学；通过惯性约束聚变获得能源；通过开发关键技术，促进对新型辐射源的更深入了解和更广泛地开发；通过提高高强度激光设施的能力，建立研究基础设施。

High-intensity laser interactions with matter produce extreme environments with very high temperatures and densities such that the electrons within the atoms of the material no longer remain bound to the atomic nuclei and the material becomes a plasma. These interactions can create conditions for studying astrophysical phenomena, including supernova shocks and solar flares, as well as supporting very high electric fields that can be used to accelerate charged particles over distances 100s to 1000s times shorter than the limits of radio-frequency accelerator technology. These compact accelerators have been shown to generate ion beams with highly desirable properties for key applications in materials testing, radiobiology, and inertial fusion energy. So far, full exploration and exploitation of these interactions has been hampered by the difficulty in reproducing their complex behaviour in numerical and computational models and by the limited data available which is caused by the low repetition rate of the high-energy pulsed laser (typically <<0.002 Hz - a shot every 10 mins) used to create the plasma and drive particle acceleration. This is particularly the case in the study of fragile ultra-thin opaque targets where the absorption of energy from the laser causes the target to heat and expand leading to the target becoming transparent as the density falls. When this occurs the laser can propagate through the target and the transfer of laser energy to the plasma is no-longer localised at the target surface. This interaction is of significant interest as it is here that the highest energy laser-accelerated protons have been recorded. A new generation of multi-Hz high-energy laser-technology is facilitating orders of magnitude increase in data acquisition rate. In order to exploit these new lasers, it is also necessary to test target technology that can provide fresh ultra-thin foils with high positional stability at multi-Hz repetition rate. In addition, despite the enormous increase in data acquisition-rate the dependence of the interaction dynamics on a large number of variables (e.g. laser energy, laser spatial and temporal energy distribution, target density profile) means that `grid-scanning' each parameter is not an efficient method to map their interdependence. By incorporating machine learning tools the high data rate enabled by the lasers and target can be used to intelligently sample the parameter space to model the interaction and quantify the stability of these novel accelerators. The proposed collaboration will address this challenge by coupling a liquid sheet target, developed at the US SLAC National Accelerator Laboratory (SLAC), with a new computer-guided approach to laser-plasma experiments, pioneered by researchers at Queen's University Belfast (QUB). The development of this novel experimental platform will enable deeper understanding of the key energy transfer pathways between laser and plasma and their dependence on experimental variables. The research will directly impact on plasma modelling, advanced accelerator research, plasma astrophysics, inertial confinement fusion, materials testing and FLASH radiobiology. The research outputs will feed into EPSRC 2022-2025 strategic priorities on the physical and mathematical sciences powerhouse, frontiers in engineering and artificial intelligence up-skilling through the research themes: AI and Data Science for Engineering, Health and Government by exploiting AI for experimental science; Energy through inertial confinement fusion; Plasma and lasers by developing crucial technology to facilitate deeper understanding and broader exploitation of novel radiation sources; and research infrastructure by enhancing the capabilities of high-intensity laser facilities.