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.
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