Convolutional neural networks for challenges in automated nuclide identification

Anthony N. Turner, Carl Wheldon, Tzany Kokalova Wheldon, Mark R. Gilbert, Lee W. Packer, Jonathan Burns, Martin Freer

Research output: Contribution to journalArticlepeer-review

145 Downloads (Pure)

Abstract

Improvements in Radio-Isotope IDentification (RIID) algorithms have seen a resurgence in interest with the increased accessibility of machine learning models. Convolutional Neural Network (CNN)-based models have been developed to identify arbitrary mixtures of unstable nuclides from gamma spectra. In service of this, methods for the simulation and pre-processing of training data were also developed. The implementation of 1D multi-class, multi-label CNNs demonstrated good generalisation to real spectra with poor statistics and significant gain shifts. It is also shown that even basic CNN architectures prove reliable for RIID under the challenging conditions of heavy shielding and close source geometries, and may be extended to generalised solutions for pragmatic RIID.
Original languageEnglish
Article number5238
Number of pages13
JournalSensors
Volume21
Issue number15
DOIs
Publication statusPublished - 3 Aug 2021

Keywords

  • GEANT
  • convolutional neural network
  • gamma spectrometry
  • nuclear applications
  • radio-isotope identification
  • simulations

Fingerprint

Dive into the research topics of 'Convolutional neural networks for challenges in automated nuclide identification'. Together they form a unique fingerprint.

Cite this