Using Deep Learning to Investigate Laboratory Astrophysics Experiments Through Collective Thomson Scattering Analysis

ORAL

Abstract

As we begin to enter a new paradigm of massive data collection and availability for high energy density science, AI and machine learning have become great candidates for the large scale data analysis needed in these experiments.

Here we present our work using a deep neural network (DNN) surrogate model to analyze the ion acoustic wave (IAW) feature from a Thomson scattering (TS) image for a control shot in a laboratory astrophysics campaign at the OMEGA Laser Facility. To train the DNN, a large dataset of Thomson scattered light spectra is generated from a multi-species 3-Maxwellian plasma model for a variety of plasma conditions using the open-source code PlasmaPy. We show the DNN predictions are comparable to results from two popular analysis methods; a 1D hybrid (kinetic ions and fluid electrons) Particle-In-Cell simulation using the code CHICAGO, and a Markov Chain Monte Carlo (MCMC) analysis of the TS data.

*This work was supported by the DOE, NNSA Center of Excellence, Center for Matter under Extreme Conditions under Award No. DE-NA000384. This work was performed under the auspices of the U.S. Department of Energy by Lawrence Livermore National Laboratory under Contract DE-AC52-07NA27344. LLNL-ABS-851237

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Presenters

  • Michael Pokornik

    • University of California, San Diego
    • Lawrence Livermore National Laboratory, Livermore, CA

Authors

  • Michael Pokornik

    • University of California, San Diego
    • Lawrence Livermore National Laboratory, Livermore, CA

  • Mario Manuel

    • General Atomics - San Diego

  • Kasper Moczulski

    • University of Rochester

  • Petros Tzeferacos

    • University of Rochester

  • Frederico Fiuza

    • Instituto Superior Tecnico (Portugal)

  • Farhat Beg

    • University of California, San Diego
    • University of California San Diego
    • Center for Energy Research UC San Diego, San Diego, CA 92093

  • Alexey V Arefiev

    • University of California, San Diego

  • E. R Tubman

    • Imperial College London
    • Imperial College
    • Imperial College London, London, UK

  • David Larson

    • Lawrence Livermore Natl Lab

  • Bradley B Pollock

    • Lawrence Livermore Natl Lab

  • George F Swadling

    • Lawrence Livermore Natl Lab

  • Drew Higginson

    • Lawrence Livermore Natl Lab
    • Lawrence Livermore National Laboratory

  • Hye-Sook Park

    • LLNL