Automatic identification and quantification of γ-emitting radionuclides with spectral variability using a hybrid Machine Learning unmixing method

Abstract

Automatic identification and quantification of γ-emitting radionuclides, considering the spectral deformation due to γ-interactions in the radioactive source environment, is a demanding challenge in the field of nuclear physics. In this context, this paper presents a hybrid unmixing approach combining a pre-trained machine learning model (autoencoder) to capture spectral deformations and a model selection technique based on statistical testing to identify the radionuclides present in a radioactive source and quantify their mixing weights. The identification process of radionuclides is based on a sequential selection algorithm using a likelihood ratio test depending on the expected false positives. Basically, this method aims to minimize the number of radionuclides in an initial radionuclide library containing characteristic γ-spectra of each γ-emitter to be tested. The robustness of decision-making with this approach and the quantification performance are investigated with Monte Carlo simulations involving up to 12 radionuclides to be tested, according to different mixture scenarios with increasing complexity and various statistics. The false positive rates obtained with the hybrid unmixing approach are close to the expected values. In general, the quantification results are similar to the case when the radionuclides present in the source and spectral signatures are known. This highlights the effectiveness of this novel hybrid unmixing approach for the automatic identification and quantification of gamma-ray emitting radionuclides with spectral variability.