Neural Networks for Rapid Analysis of High Repetition Rate Diagnostics

While many repetition rate capable PW-class laser facilities (0.1-10 Hz) are now online across the world, most are unable to regularly operate at their designed shot rate due to the lack of diagnostics that can operate at comparable rates. Diagnostic analysis is also a manual human-supervised process that is often time-consuming even with a well-designed algorithm. It would be favorable to intelligently operate experiments with software, however, this requires rapid and robust solutions to perform diagnostic analysis "on the fly" at rates higher than the laser operating frequency. Neural networks (NN's) have proven to be very powerful tools for image classification, object recognition, natural language processing, and more recently in the sciences. Here we present a proof-of-principle methodology for developing a NN surrogate for rapidly recovering metrics of interest from a rep-rate-compatible diagnostic for laser-accelerated MeV proton beams that can then be used to analyze images and return the beam characteristics such as spectral temperature, spatial profile, and total energy contained within the beam. This enables recovery of this information on the millisecond timescale, compatible with current generation high rep-rate lasers, with an average error of approximately 3%.