Machine learning applied to laser-plasma interactions.

With the advent of high repetition rate laser systems laser-plasma physics moves into a new regime,
where large amounts of data can be collected and used to test and refine our understanding of the
underlying complex nonlinear phenomena. The new BISHOP wrapper (developed by collaborators at
the University of Strathclyde) around the EPOCH particle-in-cell code automates the process of
running a large number of simulations and so provides an ideal tool to explore parameter space for
comparison to experiments producing high volumes of data. In collaboration with Strathclyde, we
will use this framework to investigate particle acceleration and radiation generation in laser solid
interactions, with BISHOP honing in on interesting regions of parameter space. The student will
investigate these regimes experimentally, focusing on the diagnostic challenge posed by high
repetition rate. Synthetic diagnostics will be included in EPOCH to help us design the real
diagnostics and understand their requirements. Examples include electron & ion spectrometers and
x-ray diagnostics.