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.
DOI
https://doi.org/10.1364/AO.444093
Source Publication
Applied Optics
Recommended Citation
Ashwin P. Rao, Phillip R. Jenkins, John D. Auxier, Michael B. Shattan, and Anil K. Patnaik, "Development of advanced machine learning models for analysis of plutonium surrogate optical emission spectra," Appl. Opt. 61, D30-D38 (2022) https://opg.optica.org/ao/abstract.cfm?URI=ao-61-7-D30
Comments
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The accepted manuscript version of this article may be posted on AFIT Scholar after January 2023.