Exploration of genetic algorithms to build a balanced neutron spectra dataset useful to train unfolding techniques based on artificial neural networks
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 i...
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| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
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EDP Sciences
2024-01-01
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| Series: | EPJ Web of Conferences |
| Online Access: | https://www.epj-conferences.org/articles/epjconf/pdf/2024/12/epjconf_snamc2024_17011.pdf |
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| _version_ | 1850206947454746624 |
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| author | Bouhadida Maha Hmede Rodayna Brovchenko Mariya Monange Wilfried Vinchon Thibaut Ducasse Quentin Trompier François |
| author_facet | Bouhadida Maha Hmede Rodayna Brovchenko Mariya Monange Wilfried Vinchon Thibaut Ducasse Quentin Trompier François |
| author_sort | Bouhadida Maha |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-bbd2cee0be69412eb77c42f8e232c960 |
| institution | OA Journals |
| issn | 2100-014X |
| language | English |
| publishDate | 2024-01-01 |
| publisher | EDP Sciences |
| record_format | Article |
| series | EPJ Web of Conferences |
| spelling | doaj-art-bbd2cee0be69412eb77c42f8e232c9602025-08-20T02:10:39ZengEDP SciencesEPJ Web of Conferences2100-014X2024-01-013021701110.1051/epjconf/202430217011epjconf_snamc2024_17011Exploration of genetic algorithms to build a balanced neutron spectra dataset useful to train unfolding techniques based on artificial neural networksBouhadida Maha0Hmede Rodayna1Brovchenko Mariya2Monange Wilfried3Vinchon Thibaut4Ducasse Quentin5Trompier François6Institut de Radioprotection et de Sûreté Nucléaire (IRSN)Institut de Radioprotection et de Sûreté Nucléaire (IRSN)Institut de Radioprotection et de Sûreté Nucléaire (IRSN)Institut de Radioprotection et de Sûreté Nucléaire (IRSN)Institut de Radioprotection et de Sûreté Nucléaire (IRSN)Institut de Radioprotection et de Sûreté Nucléaire (IRSN)Institut de Radioprotection et de Sûreté Nucléaire (IRSN)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.https://www.epj-conferences.org/articles/epjconf/pdf/2024/12/epjconf_snamc2024_17011.pdf |
| spellingShingle | Bouhadida Maha Hmede Rodayna Brovchenko Mariya Monange Wilfried Vinchon Thibaut Ducasse Quentin Trompier François Exploration of genetic algorithms to build a balanced neutron spectra dataset useful to train unfolding techniques based on artificial neural networks EPJ Web of Conferences |
| title | Exploration of genetic algorithms to build a balanced neutron spectra dataset useful to train unfolding techniques based on artificial neural networks |
| title_full | Exploration of genetic algorithms to build a balanced neutron spectra dataset useful to train unfolding techniques based on artificial neural networks |
| title_fullStr | Exploration of genetic algorithms to build a balanced neutron spectra dataset useful to train unfolding techniques based on artificial neural networks |
| title_full_unstemmed | Exploration of genetic algorithms to build a balanced neutron spectra dataset useful to train unfolding techniques based on artificial neural networks |
| title_short | Exploration of genetic algorithms to build a balanced neutron spectra dataset useful to train unfolding techniques based on artificial neural networks |
| title_sort | exploration of genetic algorithms to build a balanced neutron spectra dataset useful to train unfolding techniques based on artificial neural networks |
| url | https://www.epj-conferences.org/articles/epjconf/pdf/2024/12/epjconf_snamc2024_17011.pdf |
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