High repetition rate optimisation and control of laser-driven radiation sources

The project aims to take major steps towards addressing the challenge to optimise, stabilise and control particle and radiation generation in laser-solid interactions via the development and application of a new machine-learning platform. The specific objectives are to:
-Investigate the application of machine learning driven optimisation techniques (Bayesian optimisation, Neural Networks) to the optimisation of laser-plasma radiation sources.
-Develop and test a new machine learning platform based on particle in cell simulations of the laser-plasma interaction physics.
-Implement and demonstrate the new approach using the 350TW SCAPA laser (in-house at Strathclyde) and at EPAC laser facility at the Rutherford Appleton Laboratory, using direct experimental feedback and a simulation generated surrogate model to optimise laser-driven radiation sources.
The student will begin by acquiring knowledge of laser-plasma interaction physics, numerical simulations, machine learning techniques and experimental implementation skills. The project will continue with the student investigating the optimisation of laser-driven radiation sources using a combination of machine learning techniques and particle in cell simulations. Identifying and testing the best approach to use will be part of the project. This will help inform the feasibility of enhanced control and optimisation for experimental demonstration. The machine learning approach will then be applied to a live experiment using the 350TW SCAPA laser, and if available, the new EPAC laser.
During the project, the student to contribute to be several experimental campaigns planned to be conducted at the SCAPA beamlines Bunker B target station, on the optimisation of a high repetition rate laser-driven ion source. The student will take a key role in the planning and delivery of the experiment. Additionally, the student will participate in collaborative experiments with DSTL researchers at external research facilities, such as the Central Laser Facility.