Machine-Learning-Assisted Analysis of Visible Spectroscopy in Pulsed-Power-Driven Plasmas

Abstract

We use machine-learning (ML) models to predict ion density and electron temperature from visible emission spectra, in a high-energy density pulsed-power-driven aluminum plasma, generated by an exploding wire array. Radiation transport simulations, which use spectral emissivity and opacity values generated using the collisional-radiative code PrismSPECT, are used to determine the spectral intensity generated by the plasma along the spectrometer’s line of sight (LOS). The spectra exhibit Al-II and Al-III lines, whose line ratios and line widths vary with the density and temperature of the plasma. These calculations provide a 2500-size synthetic dataset of 400-D intensity spectra, which is used to train and compare the performance of multiple ML models on a three-variable regression task. The AutoGluon model performs best, with an R2 -score of roughly 98% for density and temperature predictions. Simpler models random forest (RF), k -nearest neighbor (KNN), and deep neural network (DNN) also exhibit high R2 -scores ( > 90% ) for density and temperature predictions. These results demonstrate the potential of ML in providing rapid or real-time analysis of emission spectroscopy data in pulsed-power-driven plasmas.