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

POSTER

Abstract



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.

Publication: Zhang, et al., "Two-dimensional Thomson scattering in high-repetition-rate laser-plasma experiments," arXiv:2305.07843 (2023).

Presenters

  • Sam Eisenbach

    UCLA

Authors

  • Sam Eisenbach

    UCLA

  • Derek Mariscal

    Lawrence Livermore Natl Lab, Lawrence Livermore National Laboratory

  • Derek B Schaeffer

    University of California, Los Angeles, University of California Los Angeles

  • Haiping Zhang

    University of California, Los Angeles

  • Jessica J Pilgram

    University of California, Los Angeles

  • Carmen G Constantin

    UCLA, University of California, Los Angeles

  • Lucas Rovige

    University of California, Los Angeles

  • Peter V Heuer

    Laboratory for Laser Energetics

  • Sophia Ghazaryan

    University of California, Los Angeles

  • Marietta Kaloyan

    University of California, Los Angeles

  • Robert S Dorst

    University of California, Los Angeles

  • Christoph Niemann

    University of California, Los Angeles