Exploiting High Repetition Rate Experiments In Dynamic High-Pressure Physics.

在动态高压物理中利用高重复率实验。

• 批准号: 2742114
• 金额: --
• 依托单位: University of York
• 依托单位国家: 英国
• 项目类别: Studentship
• 财政年份: 2022
• 资助国家: 英国
• 起止时间: 2022 至 无数据
• 项目状态: 未结题
• 来源: https://gtr.ukri.org/projects?ref=studentship-2742114
• 关键词: Exploiting; Repetition; Rate; Experiments; Dynamic

My project will be based on using machine learning techniques to analyse X-ray diffraction patterns produced from the responses of a variety of materials to laser-based dynamic compression, performed at high repetition rates. The experimental data will be produced using facilities such as the European XFEL. These facilities produce laser-like X-ray beams on the femtosecond timescale. These energetic pulses are used to reveal how materials deform at high pressure. These experiments can therefore be used to reveal the characteristics of early compression stages of ICF capsules, explore properties of materials at the cores of planets, and understand the processes that take place within materials during high strain-rate mechanical failures. Using machine learning we aim to quickly analyse the results of these experiments, with the aim that data analysis will keep pace with the high repetition rates now possible on new XFEL devices. This will hopefully guide the experiments to more fruitful parameters and regimes, detecting physics that would otherwise be missed, as well as greatly reducing poor quality or failed shots

我的项目将基于使用机器学习技术来分析各种材料对以高重复率执行的基于激光的动态压缩的响应所产生的 X 射线衍射图案。实验数据将使用欧洲 XFEL 等设施产生。这些设施在飞秒时间尺度上产生类似激光的 X 射线束。这些高能脉冲用于揭示材料在高压下如何变形。因此，这些实验可用于揭示 ICF 胶囊早期压缩阶段的特征，探索行星核心材料的特性，并了解高应变率机械故障期间材料内部发生的过程。我们的目标是利用机器学习快速分析这些实验的结果，以便数据分析能够跟上新 XFEL 设备目前可能实现的高重复率。这有望指导实验获得更富有成效的参数和制度，检测否则会错过的物理现象，并大大减少质量差或失败的镜头