Accelerating diagnostic analysis using non-surrogate machine learning for improved understanding of Thomson scattering*

Over the past 30 years Thomson scattering has become one of the premier diagnostics for temperature and density measurements at large multiple-beam laser facilities. With improved diagnostic expertise and modeling capabilities it is now possible to extract more information Thomson scattering such as the electron distribution function [1], heat transport [2], and local current fluctuations [3]. Speeding up diagnostic analysis allows deeper investigation of the data and more scientists to utilize the data in their experiments.

 

Thomson scattering is typically analyzed by matching a model spectral density function to lineouts of the measured data often using gradient-descent-based optimization. However, this approach suffers from the curse of dimensionality, making it increasingly difficult to extract more information. A new algorithm that leverages the use of GPUs and automatic differentiation allows for efficient gradient calculation and significantly increased computation speed. These techniques enable 10-100x faster parameter estimation from Thomson scattering in HED plasmas. This improved algorithm also allows estimation of additional parameters at minimal additional cost, resulting in more information from a single spectrum in less time.

References 

[1] A. L. Milder et al., Phys. Rev. Lett. 127, 015001 (2021)

[2] R. J. Henchen et al., Phys. Rev. Lett. 121, 125001 (2018)

[3] C. Bruulsema et al., Phys. Plasmas 27, 052104 (2020)