Exploiting High Repetition Rate Experiments In Dynamic High-Pressure Physics.

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My project will be based on using machine learning techniques to analyse X-ray diffraction patterns produced from the responses of a variety of materials to laser-based dynamic compression, performed at high repetition rates. The experimental data will be produced using facilities such as the European XFEL. These facilities produce laser-like X-ray beams on the femtosecond timescale. These energetic pulses are used to reveal how materials deform at high pressure. These experiments can therefore be used to reveal the characteristics of early compression stages of ICF capsules, explore properties of materials at the cores of planets, and understand the processes that take place within materials during high strain-rate mechanical failures. Using machine learning we aim to quickly analyse the results of these experiments, with the aim that data analysis will keep pace with the high repetition rates now possible on new XFEL devices. This will hopefully guide the experiments to more fruitful parameters and regimes, detecting physics that would otherwise be missed, as well as greatly reducing poor quality or failed shots