High repetition rate diagnostics with integrated machine learning analysis

High repetition rate intense short-pulse and high-energy long-pulse lasers are already online around the world and promise to revolutionize the way HED physics is done by increasing shot rates by more than three orders of magnitude. We will detail progress on our mission to develop high repetition rate short-pulse HED plasma diagnostics, that can run at a commensurate rate as these new lasers.  These diagnostics must be designed to be robust to the unique perils of HED environments such as EMP and high radiation.

 

We will outline progress on x-ray, proton, and fast electron diagnostics, and our attempts to run them simultaneously; some of the challenges we have come across when fielding at current state of the art facilities; and present preliminary results from our first round of testing at US and UK based laser facilities. We describe how such multimodal diagnostic suites can be integrated with automated analysis through the application of machine learning that will enable active feedback loops to directly control driver input and build a fully integrated and automated experimental system for investigating high intensity laser solid interactions.

 

This work was performed under the auspices of the U.S. Department of Energy by the Lawrence Livermore National Laboratory under Contract DE-AC52-07NA27344 and supported under DOE FES Measurements Innovations grant SCW1720, DOE Early Career grant SCW1651-1, and with funding support from the Laboratory Directed Research and Development Program under tracking codes 20-ERD-048 and 21-ERD-015. The CSU laser facility is supported by DOE LaserNet US (DE-SC0019076).