Development of advanced machine learning models for analysis of plutonium surrogate optical emission spectra

This work investigates and applies machine learning paradigms seldom seen in analytical spectroscopy for quantification of gallium in cerium matrices via processing of laser-plasma spectra. Ensemble regressions, support vector machine regressions, Gaussian kernel regressions, and artificial neural network techniques are trained and tested on cerium-gallium pellet spectra. A thorough hyperparameter optimization experiment is conducted initially to determine the best design features for each model. The optimized models are evaluated for sensitivity and precision using the limit of detection (LoD) and root mean-squared error of prediction (RMSEP) metrics, respectively. Gaussian kernel regression yields the superlative predictive model with an RMSEP of 0.33% and an LoD of 0.015% for quantification of Ga in a Ce matrix. This study concludes that these machine learning methods could yield robust prediction models for rapid quality control analysis of plutonium alloys. (C) 2022 Optica Publishing Group

Automated summary

This work investigates and applies machine learning paradigms rarely seen in analytical spectroscopy to quantify gallium in cerium matrices. Through optimization of model design and hyperparameter experiments, the best predictive model is obtained. Gaussian kernel regression demonstrates excellent accuracy and sensitivity in prediction. This study suggests that these machine learning methods can be applied for rapid quality control analysis of plutonium alloys.