Machine learning analysis of high-repetition rate 2-dimensional Thomson scattering spectra from laser-plasmas

With the emergence of high-repetition rate two-dimensional Thomson scattering (TS) measurements, improving data analysis is a key area of interest. We present a new way to analyze the temperature and density of laser-driven blast waves in plasmas from their TS spectra with machine learning (ML). This analysis occurs in both the collective (α << 1) and non-collective (α > 1) regimes with the goal of more accurately determining Te and ne both where spectral data has been collected and to give the ability to predict these attributes in regions where data has not been collected. We compare both the speed and accuracy of the ML model with the conventional TS inversion algorithms in the open source PlasmaPy python package.