Enabling high-fidelity spectroscopic analysis of plutonium with machine learning

Abstract

Machine learning methods are constructed to perform analysis of plutonium surrogate material. Decision tree based methods yield predictive models for quantifying gallium from optical emission spectra with sensitivities as low as 0.006 wt%.