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Poster communications
Statistical learning approaches applied to the calculation of scaling factors for radioactive waste characterization
Abstract : Radiological characterization is needed to dispose of the radioactive waste produced in high energy particle accelerators. We applied statistical learning methods to predict the activity of Difficult-to-Measure radionuclides-which are low-energy X , α-and β-emitters-, to establish criteria for sorting radioactive waste and to quantify prediction errors. Introduction DTM Difficult-to-Measure nuclides cannot be easily quantified by non-destructive assay means. Their activity a DTM is often correlated to the concentration of γ-emitters.
https://hal-cnam.archives-ouvertes.fr/hal-02507380
Contributor : Gilbert Saporta <>
Submitted on : Friday, March 13, 2020 - 10:12:57 AM
Last modification on : Friday, March 27, 2020 - 9:17:50 AM
Document(s) archivé(s) le : Sunday, June 14, 2020 - 1:37:06 PM
Contributor : Gilbert Saporta <>
Submitted on : Friday, March 13, 2020 - 10:12:57 AM
Last modification on : Friday, March 27, 2020 - 9:17:50 AM
Document(s) archivé(s) le : Sunday, June 14, 2020 - 1:37:06 PM
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