{"title":"Enabling high-fidelity spectroscopic analysis of plutonium with machine learning","authors":"A. Rao, Phillip R. Jenkins, A. Patnaik","doi":"10.1364/lacsea.2022.lf1c.1","DOIUrl":null,"url":null,"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%.","PeriodicalId":231405,"journal":{"name":"Optical Sensors and Sensing Congress 2022 (AIS, LACSEA, Sensors, ES)","volume":"170 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Optical Sensors and Sensing Congress 2022 (AIS, LACSEA, Sensors, ES)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1364/lacsea.2022.lf1c.1","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
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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%.
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