Chemical and Isotopic Determination from Complex Spectra

Challenges for proliferation detection include remote, high-sensitivity detection of chemical effluents from suspect facilities and enhanced detection sensitivity for nuclear material. Both the identification of chemical effluents with lidar and enhanced nuclear material detection from radiation sensors involve determining constituents from complex spectra. In this paper, we extend techniques used to analyze time series to the analysis of spectral data. Pattern identification methods are applied to spectral data for domains where standard matrix inversion may not be suitable because of detection statistics. We use a feed-forward, back-propagation neural network in which the nodes of the input layer are fed with the observed spectral data. The nodes of the output layer contain the identification and concentration of the isotope or chemical effluent the sensor is to identify. We will discuss the neural network architecture, together with preliminary results obtained from the training process.