A new NaI(Tl) four-detector layout for field contamination assessment using artificial neural networks and the Monte Carlo method for system calibration

An NaI(Tl) multidetector layout combined with the use of Monte Carlo (MC) calculations and artificial neural networks(ANN) is proposed to assess the radioactive contamination of urban and semi-urban environment surfaces. A very simple urban environment like a model street composed of a wall on either side and the ground surface was the study case. A layout of four NaI(Tl) detectors was used, and the data corresponding to the response of the detectors were obtained by the Monte Carlo method. Two additional data sets with random values for the contamination and for detectors' response were also produced to test the ANNs. For this work, 18 feedforward topologies with backpropagation learning algorithm ANNs were chosen and trained. The results showed that some trained ANNs were able to accurately predict the contamination on the three urban surfaces when submitted to values within the training range. Other results showed that generalization outside the training range of values could not be achieved. The use of Monte Carlo calculations in combination with ANNs has been proven to be a powerful tool to perform detection calibration for highly complicated detection geometries. (C) 2010 Elsevier B.V. All rights reserved.