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

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’s blocks while the second is a modified version of the U-net architecture. A large and balanced dataset is simulated following “realistic” physical constraints to train the architectures in an efficient way. Results show a high accuracy prediction of neutron spectra ranging from thermal up to fast spectrum. The dataset processing, the attention paid to performances’ metrics and the hyper-optimization are behind the architectures’ robustness.

Download paper here