Machine Learning for Charged Particle Imaging Applications

自1987年首次演示以来，用于分子动力学研究的带电粒子成像已经极大地改变了气相光化学和分子散射的实验。自它出现以来，人们对这项技术进行了许多改进，其中最流行的就是速度图成像(VMI)。这些实验使用激光脉冲探测化学过程(即光解离)并使光碎片电离。这产生了反冲分布，然后通过使用静电透镜组件将其投射到位置敏感探测器上。生成的图像随后被相机捕获，提供了一个极其有用的多路能量和角度分辨率数据来源，可以对气相分子的光化学动力学产生大量洞察。可以在这些图像上实施大量的后处理技术以用于多种应用，并且使用机器学习技术可以显著增强这些变换(例如去除噪声或超分辨率成像)中的许多：与通过预定算法转换输入数据以产生特定输出的传统计算不同，机器学习通过使用许多已知的输入/输出组合来切换这一过程，以替代地开发实际的变换算法。一旦这个“训练”阶段完成，就可以根据需要处理额外的输入图像。这使我们能够解决否则会非常具有挑战性的问题--特别是在无法进行解析数学解的情况下。以前利用机器学习的VMI应用包括从实验数据中去除噪声[ChemPhysChem，22，76，(2021)]，以及仅从单个2D投影[Rev.Sci]“重新膨胀”原始的3D离子/电子球状分布的能力。Instrum，93,023303，(2022年)]。拟议的博士项目将在这项工作的基础上，开发更多的神经网络，以解决与带电粒子图像处理相关的更广泛的问题。例如，这包括用于下一代成像探测器的图像上采样、真空紫外光谱区域中偏振诊断的新策略以及对时间域中的数据稀疏图像进行去噪。这将涉及广泛的计算/数值建模工作，以准确模拟相关的VMI训练数据，以及实施各种神经网络结构。此外，该项目将包括一些实验性的VMI测试数据的获取，以帮助验证正在开发的网络。

Charged particle imaging for molecular dynamics studies has significantly revolutionised experiments in gas-phase photochemistry and molecular scattering since its initial demonstration in 1987. Many improvements to this technique have been developed since its emergence, the most popular of which is known as velocity map imaging (VMI). These experiments use laser pulses to probe chemical processes (i.e. photodissociation) and ionise the photofragments. This creates a recoiling distribution which is then projected onto a position sensitive detector by use of an electrostatic lens assembly. The resulting image is then captured by a camera, providing an incredibly useful source of multiplexed energy- and angle-resolved data that can yield a great deal of insight into the photochemical dynamics of gas-phase molecules. A plethora of post-processing techniques can be implemented on these images for numerous applications, and many of these transformations (such as the removal of noise, or super-resolution imaging) can be significantly enhanced using machine learning techniques: In contrast to traditional computing, where input data is transformed via a predetermined algorithm to produce a specific output, machine learning switches up this process by using many pairs of known input/output combinations to instead develop the actual transformation algorithm. Once this "training" phase is complete, additional input images may be processed as required. This allows us to tackle problems that would otherwise be very challenging - particularly where analytical mathematical solutions are not possible. Previous VMI applications exploiting machine learning include the removal of noise from experimental data [ChemPhysChem, 22, 76, (2021)] and the ability to "reinflate" the original 3D spherical distribution of ions/electrons from just a single 2D projection [Rev. Sci. Instrum., 93, 023303, (2022)]. The proposed PhD project will build upon this work and develop additional neural networks to tackle a much wider range of problems relevant to charged particle image processing. This includes, for example, image up-sampling for next generation imaging detectors, novel strategies for polarization diagnostics in the vacuum ultraviolet spectral region, and denoising data sparse images in the temporal domain. This will involve extensive computational/numerical modelling work for the accurate simulation of relevant VMI training data, as well as implementing a variety of neural network architectures. Additionally, the project will include some acquisition of experimental VMI test data to help validate the networks under development.