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Data Augmentation for Neutron Spectrum Unfolding with Neural Networks
Version 1
: Received: 13 December 2022 / Approved: 14 December 2022 / Online: 14 December 2022 (06:43:36 CET)
A peer-reviewed article of this Preprint also exists.
McGreivy, J.; Manfredi, J.J.; Siefman, D. Data Augmentation for Neutron Spectrum Unfolding with Neural Networks. J. Nucl. Eng. 2023, 4, 77-95. McGreivy, J.; Manfredi, J.J.; Siefman, D. Data Augmentation for Neutron Spectrum Unfolding with Neural Networks. J. Nucl. Eng. 2023, 4, 77-95.
Abstract
Neural networks require a large quantity of training spectra and detector responses in order to learn to solve the inverse problem of neutron spectrum unfolding. In addition, due to the under-determined nature of unfolding, non-physical spectra which would not be encountered in usage should not be included in the training set. While physically realistic training spectra are commonly determined experimentally or generated through Monte Carlo simulation, this can become prohibitively expensive when considering the quantity of spectra needed to effectively train an unfolding network. In this paper, we present three algorithms for the generation of large quantities of realistic and physically motivated neutron energy spectra. Using an IAEA compendium of 251 spectra, we compare the unfolding performance of neural networks trained on spectra from these algorithms, when unfolding real-world spectra, to two baselines. We also investigate general methods for evaluating the performance of and optimizing feature engineering algorithms.
Keywords
Detector Response Unfolding; Neutron Spectrum Unfolding; Machine Learning; Neural Network; Feature Engineering
Subject
Physical Sciences, Radiation and Radiography
Copyright: This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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