Real time optimisation of laser-matter interaction experiments using Machine Learning

使用机器学习实时优化激光与物质相互作用实验

• 批准号: 2759310
• 金额: --
• 依托单位: Imperial College London
• 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-2759310
• 关键词: Real; time; optimisation; laser; matter

The goal of this project is to explore the effectiveness of Machine Learning for the real-time control and optimisation of laser-matter experiments. The initial goal will be to maximise the number of x-ray photons produced via the process of high harmonic generation (HHG), where an intense, infra-red femtosecond laser pulse interacts with a gas sample. The student will need to set up an experimental system, where a computer changes the experimental parameters in real-time under control of a computer code while recording the x-ray flux measured with an x-ray detector (for example, the pulse intensity is computer controlled via a motorised waveplate in combination with a polariser, the gas density is computer controlled via an electronic valve etc). A variety of computational approaches will be explored, including Bayesian Optimisation, Genetic Algorithms and Neural Networks. HHG optimisation provides a useful starting point for this project since this is an important and long-standing problem that has been tackled in numerous ways in the scientific literature and will provide useful benchmarking. This would provide a foundation for exploring the control and optimisation of other experiments, e.g., the interaction of two light pulses with a molecular sample, where the products are ions and electrons measured using time-of-flight spectroscopy and velocity map imaging and one wants to maximise (or minimise) a particular ionisation or fragmentation channel.

该项目的目的是探索机器学习对激光 - 摩擦实验实时控制和优化的有效性。最初的目标是最大化通过高谐波生成过程（HHG）产生的X射线光子的数量，在该过程中，强烈的，红外的飞秒激光脉冲与气体样品相互作用。学生将需要建立一个实验系统，在该系统中，计算机在计算机代码下实时更改实验参数，同时记录用X射线检测器测量的X射线通量（例如，脉冲强度通过计算机通过电动波板组合与极化器组合控制，通过电子阀控制了气体密度。将探索各种计算方法，包括贝叶斯优化，遗传算法和神经网络。 HHG优化为该项目提供了一个有用的起点，因为这是一个重要且长期存在的问题，在科学文献中已通过多种方式解决，并将提供有用的基准测试。这将为探索其他实验的控制和优化提供基础，例如，两种光脉冲与分子样品的相互作用，其中产物是离子和电子速度光谱和速度映射成像测量的电子，并且一个人希望最大程度地（或最小化）特定的电离或片段化或片段化通道。