Merging Ensemble Simulations and High-repetition-rate experiments for Data-Driven Atomic Physics Studies

Plasma X-ray spectra contain rich information and features, such as line intensities and widths, which can be used to deduce plasma properties such as temperature and density, however, inferring this information typically requires time-consuming expert analysis coupled with detailed atomic kinetics and/or radiation hydrodynamics simulations. “Big data” generated by ensemble simulations and high-repetition-rate (HRR, >1 Hz) experiments at ultra-intense laser facilities can be coupled through machine learning in order to transform the way that atomic physics is studied in high-energy-density plasma systems. Such an approach could dramatically increase the speed of analysis and fold in uncertainties due to plasma spatio-temporal gradients and evolution. Here we present progress in developing multi-modal, neural-network-based analysis models for rapid analysis of X-ray spectra with confidence bounds to enable temperature and density parameter scans in short-pulse laser experiments.