Real-time reconstruction of high energy, ultrafast laser pulses using deep learning.

Journal: Scientific reports
Published Date: Mar 29, 2022

Abstract

We report a method for the phase reconstruction of an ultrashort laser pulse based on the deep learning of the nonlinear spectral changes induce by self-phase modulation. The neural networks were trained on simulated pulses with random initial phases and spectra, with pulse durations between 8.5 and 65 fs. The reconstruction is valid with moderate spectral resolution, and is robust to noise. The method was validated on experimental data produced from an ultrafast laser system, where near real-time phase reconstructions were performed. This method can be used in systems with known linear and nonlinear responses, even when the fluence is not known, making this method ideal for difficult to measure beams such as the high energy, large aperture beams produced in petawatt systems.

Authors

  • Matthew Stanfield
    STROBE, NSF Science and Technology Center, University of California, Irvine, CA, 92617, USA. stanfiem@uci.edu.
  • Jordan Ott
    Fowler School of Engineering, Chapman University, United States of America; Department of Computer Science, Bren School of Information and Computer Sciences, University of California, Irvine, United States of America. Electronic address: jott1@uci.edu.
  • Christopher Gardner
    STROBE, NSF Science and Technology Center, University of California, Irvine, CA, 92617, USA.
  • Nicholas F Beier
    STROBE, NSF Science and Technology Center, University of California, Irvine, CA, 92617, USA.
  • Deano M Farinella
    STROBE, NSF Science and Technology Center, University of California, Irvine, CA, 92617, USA.
  • Christopher A Mancuso
    Department of Computational Mathematics Science and Engineering, Michigan State University, East Lansing, MI, 48824, USA.
  • Pierre Baldi
    Department of Computer Science, Department of Biological Chemistry, University of California-Irvine, Irvine, CA 92697, USA.
  • Franklin Dollar
    STROBE, NSF Science and Technology Center, University of California, Irvine, CA, 92617, USA. fdollar@uci.edu.

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