☢️ Convolutional Neural Networks for Challenges in Automated Nuclide Identification od radiation ☢️

Dear colleagues,

It is a pleasure announce that a new paper has been published on the journal Sensors for the Special Issue "☢️ Measurements, Instrumentation, Sensing and Simulation Techniques for the Detection of radiation ☢️ " , guest editors: Prof. Dr. Andrea Malizia and Prof. Dr. Tzany Kokalova Wheldon.

Turner, Anthony N., Carl Wheldon, Tzany K. Wheldon, Mark R. Gilbert, Lee W. Packer, Jonathan Burns, and Martin Freer. 2021. "Convolutional Neural Networks for Challenges in Automated Nuclide Identification" Sensors 21, no. 15: 5238. https://doi.org/10.3390/s21155238

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