Applying deep-learning in gamma-spectroscopy for radionuclide identification
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2019
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INTERNATIONAL CONFERENCE ON DOSIMETRY AND ITS APPLICATIONS, 3rd
Resumo
Introduction
Neural networks, particularly deep neural networks, are used nowadays with great success in
several tasks, such as image classifi cation, 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 fi eld: gamma-spectroscopy analysis.
Using a well-known deep neural network architecture with gamma spectroscopy data, we
successfully identify the radionuclides (Am-241, Ba-133, Cd-109, Co-60, Cs-137, Eu-152, Mn-
54, Na-24 and Pb-210) contained in several experiments.
This neural network is also capable to identify different mixed radionuclide in the same source,
demonstrating that deep neural networks can be successfully applied on gamma-spectroscopy
analysis.
Methods
Using a HPGe detector to acquire several gamma spectra, from different sealed sources, we
created a dataset that was used for the training and validation of the neural network.
We created our deep neural network using python as programing language, alongside with Keras,
a deep learning framework. Applying the VGG19 network architecture, except by the last layer
which using softmax as activation function, we used sigmoid in order to allow classifi cation of
not mutually exclusive classes in the same instance.
Results
After 250 epochs of training the classifi cation error on the training and test datasets reached
a minimum, the same occurred with accuracy.
As a fi nal test we used a spectrum from a triple sealed source, containing Am-241, Cs-137 and
Co-60. As this kind of data was never seen by the network before we expect that the network
generalizes well and correctly classify the spectra as containing the three isotopes.
When applying the new data, the model correctly classifi ed the spectra as containing the tree
radionuclide.
Conclusions
The model successfully classifi es different spectra with different radionuclides and his
performance is good on never seen before data (the triple source sealed) demonstrating that
deep learning can be used on a new domain.
Como referenciar
OTERO, ANDRE G.L.; MARUMO, JULIO T.; POTIENS JUNIOR, ADEMAR J. Applying deep-learning in gamma-spectroscopy for radionuclide identification. In: INTERNATIONAL CONFERENCE ON DOSIMETRY AND ITS APPLICATIONS, 3rd, May 27-31, 2019, Lisboa, Portugal. Abstract... Lisboa, Portugal: C2TN, Instituto Superior Técnico, Universidade de Lisboa, 2019. Disponível em: http://repositorio.ipen.br/handle/123456789/31012. Acesso em: 29 Mar 2024.
Esta referência é gerada automaticamente de acordo com as normas do estilo IPEN/SP (ABNT NBR 6023) e recomenda-se uma verificação final e ajustes caso necessário.