Issue |
EPJ Web Conf.
Volume 302, 2024
Joint International Conference on Supercomputing in Nuclear Applications + Monte Carlo (SNA + MC 2024)
|
|
---|---|---|
Article Number | 17011 | |
Number of page(s) | 11 | |
Section | Artificial Intelligence & Digital in Nuclear Applications - Quantum Computing | |
DOI | https://doi.org/10.1051/epjconf/202430217011 | |
Published online | 15 October 2024 |
https://doi.org/10.1051/epjconf/202430217011
Exploration of genetic algorithms to build a balanced neutron spectra dataset useful to train unfolding techniques based on artificial neural networks
1 Institut de Radioprotection et de Sûreté Nucléaire (IRSN), Fontenay-aux-Roses 92260, France
2 Institut de Radioprotection et de Sûreté Nucléaire (IRSN), Cadarache 13115, France
* Corresponding author: maha.bouhadida@gmail.com
Published online: 15 October 2024
Diverse domains need neutrons unfolding technics to assess the incident neutron energy spectrum. Examples are radiation protection, nuclear reactor physics or criticality safety. Traditionally, methods based on the Bayesian approach requires an initial guess of the solution which may significantly impact the unfolding result. This work proposes a novel method for neutron spectrum reconstruction using machine learning (ML) techniques trained on a large dataset. To ensure the ML algorithm to perform on a large domain of application particular attention has been paid to the dataset creation. We propose a comparison of two methods of building large dataset where the most adequate solution is obtained using a dynamic genetic algorithm (GA). This GA targets optimal combinations of 48 parameters to generate a variety of neutron spectra. The resulting dataset is then used to train a new convolutional neural network architecture for unfolding neutron spectra. Obtained performance metrics of the tested architecture show high efficiency and emphasize the added value of the built dataset.
© The Authors, published by EDP Sciences, 2024
This is an Open Access article distributed under the terms of the Creative Commons Attribution License 4.0, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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