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

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.