Title

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

Document Type

Article

Publication Date

Winter 1-20-2022

Abstract

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.

Comments

The "Link to Full Text" on this page is for current Applied Optics subscribers.

Copyright statement: © 2022 Optica Publishing Group

The accepted manuscript version of this article may be posted on AFIT Scholar after January 2023.

DOI

https://doi.org/10.1364/AO.444093

Source Publication

Applied Optics

Share

COinS