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Poster communications

Statistical learning approaches applied to the calculation of scaling factors for radioactive waste characterization

Biagio Zaffora 1 Jean-Pierre Chevalier 2 Francesco La Torre 1 Catherine Luccioni 2 Matteo Magistris 1 Gilbert Saporta 3
1 CERN [Genève]
2 CEDRIC - Centre d'études et de recherche en informatique et communications
3 CEDRIC - MSDMA - CEDRIC. Méthodes statistiques de data-mining et apprentissage
CEDRIC - Centre d'études et de recherche en informatique et communications
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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Poster communications
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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
Long-term archiving on: : Sunday, June 14, 2020 - 1:37:06 PM

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Biagio Zaffora, Jean-Pierre Chevalier, Francesco La Torre, Catherine Luccioni, Matteo Magistris, et al.. Statistical learning approaches applied to the calculation of scaling factors for radioactive waste characterization. 14th Congress of the International Radiation Protection Association, May 2016, Cape Town, South Africa. ⟨10.13140/RG.2.1.3956.8240⟩. ⟨hal-02507380⟩

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