A Hybrid Machine Learning Unmixing Method for Gamma-Spectrometry

The automatic identification and quantification of gamma-emitting radionuclides, accounting for spectral deformation due to gamma interactions in the environment of radioactive sources, is a challenge for various nuclear applications. In this paper, this problem is addressed by developing a hybrid approach combining Machine Learning and classical statistical methods. A specific Machine Learning-based autoencoder that can capture spectral variability with limited data is proposed. A novel hybrid unmixing algorithm employing a pre-trained autoencoder is investigated for joint estimation of spectral signatures and counting in the case of mixtures of four radionuclides (⁵⁷ Co, ⁶⁰Co, ¹³³Ba, ¹³⁷ Cs), This study was carried out to take account of spectral deformation due to attenuation and Compton scattering at low statistics. The results demonstrate the validity of this new hybrid approach based on Machine Learning and Maximum Likelihood for the automatic full-spectrum analysis of gamma-spectra.