Efficient Analysis of High-Resolution Imaging Data in High-Energy Density Physics

高能量密度物理中高分辨率成像数据的高效分析

• 批准号: 2888287
• 金额: --
• 依托单位: University of Oxford
• 依托单位国家: 英国
• 项目类别: Studentship
• 财政年份: 2023
• 资助国家: 英国
• 起止时间: 2023 至 无数据
• 项目状态: 未结题
• 来源: https://gtr.ukri.org/projects?ref=studentship-2888287
• 关键词: Efficient; Analysis; Resolution; Imaging; Data

Efficient Analysis of High-Resolution Imaging Data in High-Energy Density PhysicsThis project will center around observing properties of warm dense matter. Warm dense matter is a state of matter that is too hot to be considered a solid, but too dense to be considered a plasma. It occurs in planetary interiors, as well as during inertial confinement fusion experiments. In order to model and understand these complex environments, certain properties of warm dense matter must be experimentally measured, such as its thermal conductivity. This knowledge could aid in the modelling of planetary evolution, as well as in the design of fusion capsules. The main goal of the project is to observe the thermal conductivity of various metal-plastic interfaces. A novel technique will be developed and employed in the process of analyzing the X-ray images. This measurement of thermal conductivity will be done using data already collected at the Linac Coherent Light Source, or LCLS, which is an X-ray Free Electron Laser (XFEL). The experiment also utilized an optical laser. The optical laser was used to heat a plastic-coated metal wire to warm dense matter conditions, where it was then imaged by the XFEL. By repeating the experiment with various different time delays between the two lasers, the wire's expansion can be observed through time. Simulating the dynamics of the target can give information about its thermal conductivity.Observing the evolution of warm dense matter systems is a difficult process. When creating these systems in a lab, measurements are limited by the small size and short duration in which the system we are observing exists. Therefore, few experimental values of the thermal conductivity of warm dense matter exist, and any new measurements would be valuable for benchmarking theoretical models. During this process of extracting a thermal conductivity measurement, a novel technique involving machine learning will be utilized. Currently, a forward model exists that can simulate a diffraction pattern created from a given density profile. The inverse of this process is much harder. We propose using an untrained neural network in combination with the forward model as a tool to extrapolate the density profile given its diffraction pattern. The neural network will be trained to output a density profile, which will then be run through a diffraction code and compared to the experimental results. In this way, the neural network can be trained to minimize the difference between the simulated image and the experimental data. Finally, the density profiles can be matched to hydrodynamic simulations, which would allow for the measurement of thermal conductivity.This project falls within the EPSRC Plasma and Lasers research area. It involves the investigation of a high density plasma, created by a short pulse laser. The experimental measurement of the thermal conductivity of warm dense matter will bolster the design of inertial confinement fusion experiments.CollaboratorsSLAC Tom White, UNRMatthew Oliver, STFCDan Eakins, University of Oxford

高能量密度物理学中高分辨率成像数据的有效分析本项目将围绕观察温暖致密物质的性质进行。热致密物质是一种物质状态，它太热而不能被认为是固体，但密度太大而不能被认为是等离子体。它发生在行星内部，以及在惯性约束聚变实验期间。为了模拟和理解这些复杂的环境，必须通过实验测量温暖致密物质的某些性质，例如其热导率。这些知识可以帮助建立行星演化的模型，以及设计聚变舱。该项目的主要目标是观察各种金属-塑料界面的热导率。一种新的技术将被开发并应用于分析X射线图像的过程中。热导率的测量将使用直线加速器相干光源（LCLS）（X射线自由电子激光器（XFEL））已经收集的数据来完成。该实验还使用了光学激光器。光学激光器被用来加热塑料涂层的金属线，以温暖致密物质的条件，然后由XFEL成像。通过在两个激光器之间使用各种不同的时间延迟重复实验，可以通过时间观察金属丝的膨胀。模拟目标的动力学可以提供有关其热导率的信息，而观测温暖的稠密物质系统的演化是一个困难的过程。当在实验室中创建这些系统时，测量受到我们所观察的系统存在的小尺寸和短持续时间的限制。因此，很少有热致密物质的热导率的实验值存在，任何新的测量将是有价值的基准理论模型。在提取热导率测量值的过程中，将利用涉及机器学习的新技术。目前，存在可以模拟从给定密度分布创建的衍射图案的正演模型。这个过程的逆过程要困难得多。我们建议使用一个未经训练的神经网络结合的前向模型作为一种工具，外推的密度分布给定其衍射图案。神经网络将被训练以输出密度分布，然后将通过衍射代码运行并与实验结果进行比较。通过这种方式，可以训练神经网络，以最小化模拟图像与实验数据之间的差异。最后，密度分布可以与流体动力学模拟相匹配，这将允许测量热导率。该项目属于EPSRC等离子体和激光研究领域的福尔斯。它涉及到由短脉冲激光产生的高密度等离子体的研究。实验测量热致密物质的热导率将支持惯性约束聚变实验的设计。合作者SLAC Tom白色，Matthew奥利弗，STFC丹·伊金斯，牛津大学