Neutron Spectrum Unfolding using two Architectures of Convolutional Neural Networks
M. Bouhadida, A. Mazzi, M. Brovchenko, T. Vinchon, M. Z. Alaya, W. Monange, F. Trompier
Nuclear Engineering and Technology, 2023
We deploy artificial neural networks to unfold neutron spectra from measured energy-integrated quantities. These neutron spectra represent an important parameter allowing to compute the absorbed dose and the kerma to serve radiation protection in addition to nuclear safety. The built architectures are inspired from convolutional neural networks. The first architecture is made up of residual transposed convolution blocks while the second is a modified version of the U-net architecture. Read more