10.1364/LACSEA.2022.LF1C.1">
 

Enabling high-fidelity spectroscopic analysis of plutonium with machine learning

Document Type

Conference Proceeding

Publication Date

7-15-2022

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%.

Comments

The full text of this paper is available via subscription or purchase at Optica, through the DOI link below.

Co-author Ashwin Rao was completing his AFIT PhD program at the time of this publication. (AFIT-ENP-DS-22-S-048, September 2022)

Source Publication

Optical Sensors and Sensing Congress 2022 (AIS, LACSEA, Sensors, ES)

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