Inversion of simulated positron annihilation lifetime spectrum using a neural network

Inversion of positron annihilation lifetime spectroscopy, based on a neural network Hopfield model, is presented in this paper. From a previous report ed density function for lysozyme in water a simulated spectrum; without the superposition of statistical fluctuation and spectrometer resolution effec ts, was generated. These results were taken as the exact results from which the neural network was trained. The precision of the inverted density func tion was analyzed taking into account the number of neurons and the learnin g time of the neural network. A fair agreement was obtained when comparing the neural network results with the exact results. For example, the maximum of the density function, with a precision of 0.4% for the percentual relat ive error, was obtained for 64 neurons.