Highly accurate prediction of specific activity using deep learning

Mati Sheinfeld, Samuel Levinson, Itzhak Orion

Research output: Contribution to journalArticlepeer-review

11 Scopus citations

Abstract

Building materials can contain elevated levels of naturally occurring radioactive materials (NORM), in particular Ra-226, Th-232 and K-40. Safety standards, such as IAEA Safety Standards Series No. GSR Part 3, dictate particular activities that must be fulfilled to ensure adequate safety. Traditional methods include spectral analysis of material samples measured by a HPGe detector then processed to calculate the specific activity of the NORM in Bq/Kg with 1.96 σ uncertainty. This paper describes a new method that pre-processes the raw spectrum then feeds the result into a set of pre-trained neural networks, thus generating the required specific radionuclide activity as well as the 1.96 σ uncertainty.

Original languageEnglish
Pages (from-to)115-120
Number of pages6
JournalApplied Radiation and Isotopes
Volume130
DOIs
StatePublished - 1 Dec 2017

Keywords

  • Building materials
  • Deep learning
  • NORM
  • Neural networks
  • Specific activity

ASJC Scopus subject areas

  • Radiation

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