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有效分析这个项目将以观察热致密物质的性质为中心。热的致密物质是一种物质的状态，它太热而不能被认为是固体，但太致密而不能被认为是等离子体。它发生在行星内部，也发生在惯性约束聚变实验中。为了模拟和理解这些复杂的环境，必须对热致密物质的某些性质进行实验测量，例如它的导热系数。这些知识有助于建立行星演化的模型，也有助于聚变太空舱的设计。该项目的主要目标是观察各种金属-塑料界面的导热系数。在分析X射线图像的过程中，将开发和使用一种新的技术。这种热导率的测量将使用已经在直线加速器相干光源(LCLS)上收集的数据来完成，LCLS是一种X射线自由电子激光器(XFEL)。该实验还利用了一台光学激光器。光学激光被用来将塑料涂层的金属线加热到致密物质条件下，然后由XFEL成像。通过在两个激光器之间以各种不同的时间延迟重复实验，可以观察到金属丝随时间的膨胀。模拟目标的动力学可以提供关于其导热系数的信息。观测热致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，测量受到我们正在观察的系统存在的小尺寸和短持续时间的限制。因此，热致密物质导热系数的实验值很少，任何新的测量结果都将对理论模型进行基准测试。在这个提取导热系数测量的过程中，将使用一种涉及机器学习的新技术。目前，存在能够模拟从给定密度分布产生的衍射图的正演模型。这一过程的反面要困难得多。我们建议使用未训练的神经网络结合正演模型作为工具来外推给定的衍射图的密度分布。神经网络将被训练以输出密度分布，然后通过衍射代码运行该密度分布并与实验结果进行比较。这样，神经网络就可以被训练来最小化模拟图像和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，这将允许测量导热系数。该项目属于EPSRC等离子体和激光研究领域。它涉及对由短脉冲激光产生的高密度等离子体的研究。对热致密物质导热系数的实验测量将支持惯性约束聚变实验的设计。

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