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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.

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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