Determination of Plasma Density and Temperature Gradients through the X-ray Spectroscopy with Deep Learning

ORAL

Abstract

Short-pulse, laser-solid interactions provide a unique platform to develop well-characterized laboratory high-energy density (HED) matter conditions to diagnose fundamental properties such as opacity and equations of state. These measurements are needed to benchmark atomic physics models and simulations tools. However, analysis of such plasmas remains challenging due to the rapid temporal and spatial evolution of the emitting plasma. Recent work has shown that the use of deep learning can provide enhanced analysis of hard X-ray spectra relevant to HED plasmas in both speed and complexity. Here, we present the model development of a neural network trained on the collisional-radiative modeling code SCRAM that is capable of extracting the temperature and density profiles, and hot electron fraction. This model is applied to experimental data performed at Colorado State University’s ALEPH laser where high-resolution (E/ΔE > 7500) X-ray spectroscopy of copper K-shell emission was used to generate micron-scale, near solid-density plasmas with electron densities exceeding 1024 cm-3 and temperatures exceeding 3 keV.

Citations:

[1] Mariscal, D. A. et al., Enhanced analysis of experimental x-ray spectra through deep learning. Phys. Plasmas 1 September 2022; 29 (9): 093901. https://doi.org/10.1063/5.0097777

*This work was supported by the U.S. DOE Office of Science, Fusion Energy Sciences (FES) and Lawrence Livermore National Lab (LLNS Subcontract B643845, DOE/NNSA DEAC52). The LaserNetUS initiative at Colorado State University (Contract No. DE-SC-0019076 and DE-SC0021246). The U.S. DOE FES Postdoctoral Research Program administered by the Oak Ridge Institute for Science and Education (ORISE) for the DOE, the NSERC Alliance - Alberta Innovates Advance Program (Agreement No. 212201089 and 222302077), and the Natural Sciences and Engineering Research Council of Canada (grant no. RGPIN-2021-04373). This research was undertaken, in part, thanks to funding from the Canada Research Chairs Program. ORISE is managed by Oak Ridge Associated Universities under DOE contract number DE-SC0014664. SNL is managed and operated by NTESS under DOE NNSA contract DE-NA0003525.

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Publication: N. F. Beier, H. Allison, P. C Efthimion, K. A. Flippo, L. Gao, S. B. Hansen, K. Hill, R. Hollinger, M. Logantha, Y. Musthafa, R. Nedbailo, V. Senthilkumaran, R. Shepherd, V. N. Shlyaptsev, H. Song, S. Wang, F. Dollar, J. J. Rocca, and A. E. Hussein Homogeneous, Micron-Scale High-Energy-Density Matter Generated by Relativistic Laser-Solid Interactions. Physical Review Letters 129,135001 (2022)

Presenters

  • Nicholas F Beier

    • University of Alberta

Authors

  • Nicholas F Beier

    • University of Alberta

  • Matthew Maurier

    • University of Alberta

  • Uriah Martinkus

    • University of Alberta

  • Bassam Nima

    • University of Alberta

  • Vigneshvar Senthilkumaran

    • University of Alberta

  • Hunter G Allison

    • University of California, Irvine

  • Yasmeen Musthafa

    • TAE Technologies

  • Mahek Logantha

    • University of California, Irvine

  • Philip Efthimion

    • Princeton Plasma Physics Laboratory

  • Lan Gao

    • PPPL

  • Kenneth W Hill

    • Princeton Plasma Physics Laboratory
    • Princeton University

  • Kirk A Flippo

    • Los Alamos National Lab
    • Los Alamos National Laboratory

  • Stephanie B Hansen

    • Sandia National Laboratories

  • Reed C Hollinger

    • Colorado State University

  • Ryan Nedbailo

    • Colorado State University

  • Shoujun Wang

    • Colorado State University

  • Vyacheslav N Shlyaptsev

    • Colorado State University

  • Ronnie Lee Shepherd

    • Lawrence Livermore Natl Lab

  • Franklin J Dollar

    • University of California, Irvine

  • Jorge J Rocca

    • Colorado State University

  • Amina E Hussein

    • Univ of Alberta