Comparing deep learning architectures on gamma-spectroscopy analysis for nuclear waste characterization

• Autor(a) principal: OTERO, ANDRE G.L.
• Data de Publicação: 2020
• Outros Autores: POTIENS JUNIOR, ADEMAR J., CALZETA, EDUARDO P., MARUMO, JULIO T., INTERNATIONAL NUCLEAR ATLANTIC CONFERENCE
• Tipo de documento: Artigo de conferência
• Título da fonte: Repositório Institucional do IPEN
• Texto Completo: http://repositorio.ipen.br/handle/123456789/30561
• topic: artificial intelligence; computer architecture; gamma spectroscopy; high-purity ge detectors; neural networks; radioactive waste management; radioactive wastes; radioisotopes; sealed sources

Neural networks, particularly deep neural networks, are used nowadays with great success in several tasks, such as image classification, image segmentation, translation, text to speech, speech to text, achieving super-human performance. In this study, we explore the capabilities of deep learning on a new field: gamma-spectroscopy analysis, comparing the classification performance of different deep neural networks architectures. We choose VGG-16, VGG-19, Xception, ResNet, InceptionV3 and MobileNet architectures which are available through the Keras Deep Learning framework to identify several different radionuclides (Am-241, Ba- 133, Cd-109, Co-60, Cs-137, Eu-152, Mn-54, Na-24, and Pb-210). Using an HPGe detector to acquire several gamma spectra, from different sealed sources to created a dataset that was used for the training and validation of the neural networks comparison. This study demonstrates the strengths and weakness of applying deep learning on gamma-spectroscopy analysis for nuclear waste characterization.