Statistical modelling by neural networks in gamma-spectrometry

International audienceLayered Neural Networks are a class of models based on neural computation and have been applied to the measurement of uranium enrichment. The usual methods consider a limited number of $X$- and $\gamma$-ray peaks, and require calibrated instrumentation for each sample. Since the source-detector ensemble geometry conditions critically differ between such measurements, the spectral region of interest is normally reduced to improve the accuracy of such conventional methods by focusing on the $K_{\alpha}X$ region where the three elementary components are present. Such measurements lead to the desired ratio. Experimental data have been used to study the performance of neural networks involving a Maximum-Likelihood Method. The encoding of the data by a Neural Network approach is a promising method for the measurement of uranium ${}^{235}U$ and ${}^{238}U$ in infinitely thick samples